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Ergonomics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/terg20
An ergonomics study of thumb movements on smartphone touch screen a
Jinghong Xiong & Satoshi Muraki a
b
Graduate School of Design, Kyushu University, Fukuoka, Japan
b
Faculty of Design, Kyushu University, Fukuoka, Japan Published online: 08 Apr 2014.
Click for updates To cite this article: Jinghong Xiong & Satoshi Muraki (2014) An ergonomics study of thumb movements on smartphone touch screen, Ergonomics, 57:6, 943-955, DOI: 10.1080/00140139.2014.904007 To link to this article: http://dx.doi.org/10.1080/00140139.2014.904007
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Ergonomics, 2014 Vol. 57, No. 6, 943–955, http://dx.doi.org/10.1080/00140139.2014.904007
An ergonomics study of thumb movements on smartphone touch screen Jinghong Xionga* and Satoshi Murakib a
Graduate School of Design, Kyushu University, Fukuoka, Japan; bFaculty of Design, Kyushu University, Fukuoka, Japan
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(Received 8 April 2013; accepted 28 February 2014) This study investigated the relationships between thumb muscle activity and thumb operating tasks on a smartphone touch screen with one-hand posture. Six muscles in the right thumb and forearm were targeted in this study, namely adductor pollicis, flexor pollicis brevis, abductor pollicis brevis (APB), abductor pollicis longus, first dorsal interosseous (FDI) and extensor digitorum. The performance measures showed that the thumb developed fatigue rapidly when tapping on smaller buttons (diameter: 9 mm compared with 3 mm), and moved more slowly in flexion – extension than in adduction– abduction orientation. Meanwhile, the electromyography and perceived exertion values of FDI significantly increased in small button and flexion– extension tasks, while those of APB were greater in the adduction– abduction task. This study reveals that muscle effort among thumb muscles on a touch screen smartphone varies according to the task, and suggests that the use of small touch buttons should be minimised for better thumb performance. Practitioner Summary: Through measurements of electromyography, thumb performance and perceived exertion, this study reveals that demand on FDI increases when the thumb taps small buttons and in flexion– extension orientation, but that demand on APB increases in adduction –abduction orientation. The decreased thumb performance with small buttons is attributed to a combination of smaller targets and increased demand on FDI. Keywords: smartphone touch screen; thumb performance; EMG; perceived exertion
1.
Introduction
Mobile phones are now such a significant communication tool that they play an irreplaceable role in our everyday life and work. In the 10 years between 1995 and 2004, the number of mobile phone users worldwide increased from 91 million to approximately 1.75 billion (Goggin 2006). In 2008, this figure jumped up to about 4 billion (UN News Service 2008) and reached 6 billion by 2012 (International Telecommunication Union 2012). Since touch screens were introduced into mobile phones a few years ago and have since become increasingly affordable, it is believed that touch screen mobile phones will include even more users in the future. Although touch screens clearly enhance the user’s experience by offering an even more user-friendly interface than tactile keypads, there is also a need to improve the input performance for the use, such as keyboard typing. In most of the cases, when holding and typing a touch screen smartphone with one hand, the thumb is likely to undertake nearly all the operating tasks. In other words, the thumb greatly affects, even determines, the input performance on the touch screen of smartphone. To date, there have been insufficient studies regarding thumb muscles in mobile use as applied to touch screen smartphones. Trudeau et al. (2012) suggested that thumb movement in adduction– adduction is faster than in flexion– extension orientation, but their explanations for such faster movement were based on thumb motor performance and joint coordination, and thumb muscle effort was barely involved. A study found that the thumb of the right hand was the most prevalent reported source of muscle pain among users of mobile hand-held devices (Berolo, Wells, and Amik 2011), but this study did not explore the reasons for the pain, and muscle effort assessment was not covered in the study. Another study found that people enter text on mobile phones with tactile keyboards by one thumb rather than two hands, and higher muscle activities were detected in the muscle abductor pollicis longus (APL) of the thumb when typing with a faster speed (Gustafsson et al. 2011). Jonsson et al. (2011) pointed out that the muscle activity of the thumb is an ideal indicator for assessing musculoskeletal loads of mobile phone use. Moreover, Hogg (2010) reported that greater effort was perceived for the thumb when using the keypad in the right bottom corner of a mobile phone, but it was not pointed out that which thumb muscles are responsible for the increased effort. However, the thumb movements on a smartphone touch screen could greatly differ from those on mobile phone with tactile keypads (e.g. key pushing is replaced by light tapping on a flat touch screen surface). Thus, whether or not these results would match those for the use of a smartphone touch screen remains unclear.
*Corresponding author. Email: [email protected] q 2014 Taylor & Francis
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This study aimed to shed light on thumb performance in smartphone touch screen operation. By referring to the variation of muscle activity and perceived exertion evaluation in the targeted thumb muscles at three categories of thumb operating task, namely tapping (large and small buttons), moving (adduction – abduction and flexion –extension orientations) and circling (clockwise and counter-clockwise directions), this study should provide a better understanding of thumb muscle activity and its connection to thumb movements on a smartphone touch screen, and offer a knowledge base for the better design of user interfaces for touch screen smartphones, as well as for other hand-held touch screen devices.
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2.
Methods
2.1. Participants A total of 20 university students (10 males, 10 females, mean (^ standard deviation) age 24.5 ^ 2.2 years) were recruited in this study. All participants were right-handed, and they all owned touch screen smartphones for daily use, such as sending emails and posting comments on social network websites. None of the participants reported a musculoskeletal disorder or pain, nor any motor disorders or symptoms. This study was approved by the Institutional Ethics Review Board of Kyushu University, Japan, and informed consent was received from each participant.
2.2. Targeted thumb muscles Six muscles in the right thumb and forearm were targeted in this study (Figure 1), namely adductor pollicis (AP), flexor pollicis brevis (FPB), abductor pollicis brevis (APB), APL, first dorsal interosseous (FDI) and extensor digitorum (ED). The position of each muscle was marked and clearly described to the participants in the experiment.
2.3.
Experiment smartphone mock-up
The smartphone mock-up used in the experiment was a physical copy of iPhone4, a global touch screen smartphone that all of the participants claimed to be familiar with. Its dimensions were 115.2 £ 58.5 £ 9.3 mm, with a weight of 140.0 g. The tested keyboard layout also copied that from iPhone4, which was 50.0 £ 33.0 mm in size, with 20.0 mm from the bottom aligned to the centre line.
2.4.
Protocol
All participants sat comfortably in an armless chair (height was adjustable to match body height) in front of a 70-cm-high table. The participants were required to place their tested right arm on the table in a posture and position that would provide acceptable comfort (Figure 2). Thus, the arm and wrist were fully supported so that the participants were able to focus their concentration on the thumb while performing the experimental tasks. The participants were asked to hold the smartphone mock-up in a posture matching that which they normally use on a daily basis. In addition, they were allowed to shift the holding posture during the experiment to regain a comfortable position, as long as the arm and wrist remained fully supported on the table. The experiment was carried out in an indoor human kinetics laboratory, where temperature and lighting were controlled in order to provide optimal experimental conditions.
Figure 1. The six targeted thumb muscles: (1) AP – adductor pollicis, (2) FPB – flexor pollicis brevis, (3) APB – abductor pollicis brevis, (4) APL – abductor pollicis longus, (5) FDI – first dorsal interosseous, (6) ED – extensor digitorum.
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Figure 2.
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The arm and phone mock-up holding posture during the experiment.
2.5. Tasks The experiment required participants to tap round and flat buttons on the keyboard layout by moving their thumb. Participants started each subtask when a signal was given, and then stopped freely when they started to perceive fatigue in the thumb (Table 1). In order to represent the overall thumb movement in this study, the experiment was divided into three tasks, namely tapping, moving and circling tasks. Each task contained two tasks, which were ‘large button’ and ‘small button’ in the tapping task, ‘adduction – abduction’ and ‘flexion –extension’ in the moving task and ‘clockwise’ and ‘counter-clockwise’ in the circling task. Furthermore, each task was composed of two subtasks, namely ‘fixed speed’ and ‘max speed’ subtasks. In the fixed speed subtask, the participants performed one tap per second coordinated with a digital metronome. The frequency of the metronome was one hertz, and all participants claimed that its sound was clear during the experiment. In the max speed subtask, the participants were asked to tap the buttons as fast as possible. The rationale for applying these two speed subtasks is as follows. First, it was to make sure that each participant was subjected to a sufficient workload to develop a feeling of fatigue. Second, if no significant difference was found in fatigue time in the max speed subtask, then tapping speed can be regarded to represent thumb performance. In addition, a rest period (at least five minutes) was provided for the participants when a subtask was completed. The next subtask was started only when the participants claimed that the fatigue feeling from the previous subtask was clear. The buttons were white, printed on a black keyboard layout, whereas the rest of the screen surface remained white. Thus, the participants could clearly see the layout and buttons. The order of the tasks started with tapping, moving and then the circling task. However, the orders between large and small button tasks, adduction – abduction and flexion –extension tasks, as well as clockwise and counter-clockwise tasks were randomised. The order of subtasks always started with a fixed speed, and finished with the max speed subtask. 2.5.1.
Tapping task
The tapping task required participants simply to tap a round paper button (with a slim pressure sensor underneath) in the physical centre of the keyboard layout (Figure 3A). In the large button task, participants tapped a button with a diameter of
Table 1.
Experiment outline. Large button
Tapping Small button Adduction – abduction Moving Flexion – extension Clockwise Circling Counter-clockwise
Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed
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Figure 3. Tasks in three tasks: A. Tapping task: large button task – button diameters of 9.0 mm, small button task – 3.0 mm; B. Moving task: adduction – abduction task – orientation between a and c, flexion– extension task – b and d; C. Circling task: clockwise task – direction a-b-c-d, counter-clockwise task: a-d-c-b.
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9.0 mm; in the small button task, the button’s size was reduced to 3.0 mm. The distance between the target buttons (physical centre) was 15.0 mm for large button task and 5.0 mm for small button task. 2.5.2. Moving task Two tasks were included, namely adduction –abduction and flexion– extension task. In adduction –abduction task, participants tapped between the two round buttons (pressure sensors with diameter 9.0 mm) at the two end adduction– abduction orientation corners on the keyboard layout, whereas they tapped the other two end corners of flexion –extension orientation in flexion –extension task (Figure 3B). The button size was chosen at 9.0 mm in diameter to avoid the bias of tapping a small button, since it is physically harder than a large button to locate. The distance between the target buttons (physical centre) was 60.0 mm for both orientations (a – c for adduction – abduction and b– d for flexion –extension, Figure 3B). 2.5.3.
Circling task
Participants undertook two tasks, namely clockwise and counter-clockwise task. Participants tapped the four round buttons (pressure sensors with diameter 9.0 mm) at each end corner of the keyboard layout (Figure 3C). In clockwise task, the tapping direction was from top-right, down-right, down-left and then to top-left; in counter-clockwise task, the process still started at the top-right button but the direction was opposite. The distance between the target buttons (physical centre) of top-right and down-right, top-left and down-left was 33.0 mm (the width of the keyboard layout, Figure 3C), and that of topright and top-left, down-right and down-left was 50.0 mm (the length of the keyboard layout, Figure 3C). 2.6. 2.6.1.
Measurements Thumb performance
2.6.1.1. Fatigue time. The time at which participants started tapping (when the start signal was given) until they stopped was defined as the ‘fatigue time’ in this study. The time of stopping was defined as the point at which they started to perceive a feeling of fatigue in the thumb leading them to stop tapping. A shorter fatigue time indicates that the thumb becomes more uncomfortable since it develops fatigue more rapidly. 2.6.1.2 Tapping speed. Tapping speed refers to how fast the participants could tap a button in the max speed subtasks. An extremely thin pressure sensor was firmly attached (model: FlexiForce A201-1, Nitta, Japan) underneath each button and connected with the EMG recording laptop to record the tapping pressure waves and count the taps. 2.6.2. Perceived exertion evaluation All six targeted thumb muscles were included in this assessment. After each task was completed, the participants immediately filled out a Borg’s CR-10 scale form to rate their perceived level of fatigue in each targeted thumb muscle. Participants marked their perceived level of fatigue from individual number 0 –10 in the scale for each targeted muscle, while 0 stands for no feeling of fatigue and 10 stands for the maximum level. Borg’s scale is a widely used tool to assess the degree of subjective feeling of fatigue (Mamaghani et al. 2009). Wos et al. (1988) stated that Borg’s scale is highly reliable in the evaluation of hand-arm vibrations of short duration. Moreover, a study found that Borg’s CR-10 scale was highly reliable in the evaluation of subjective muscle fatigue in the radial side of the hand (thumb), when the hand was performing grip force (McGorry et al. 2010). Even though they did not describe that this tool has been used for assessing thumb
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muscles, the above studies strongly support the assertion that this tool is reliable for this study. In addition, the experiment was carefully set up. The arm holding the phone mock-up was fully supported (including the wrist) by putting it on the table with a comfortable posture, and the positions of the muscles were clearly marked and explained by instructions to the participants. When a muscle was marked, the participants were asked to tap on the phone mock-up and inform the researchers whether or not they had understood the position of the muscle. Furthermore, after all the muscles were marked, the participants were asked to tap on the phone mock-up to compare their recognition to the positions of the marked muscles, and their perception of the effort that these muscles made when tapping on the phone mock-up. Before the tasks started, all the participants claimed that they had understood the positions of the marked muscles and what to do in terms of rating their perceived exertion for the muscles. Furthermore, in order to provide a positive physical environment for the participants to maintain their concentration on the tasks, the temperature and other environmental factors were also well controlled throughout the experiment.
2.6.3. Electromyography (EMG) Four muscles, namely APB, APL, FDI and ED, were registered for EMG assessment. In APB, the electrodes were placed over the muscle belly between the metacarpophalangeal (MCP I) and carpometacarpal (CMC I) joints (Seror, Maisonobe, and Bouche 2011), while the electrodes for APL were placed on the forearm proximal to the styloid process of the radius (Gustafsson et al. 2011). In FDI, the electrodes were placed over the muscle belly above the base of the thumb and proximal to the base of the index finger, and the electrodes for ED were one-third of the distance between the epicondyle and the styloid process of the radius (Gustafsson et al. 2011). All electrodes were covered by adhesive sports bandages, so that both flexibility for thumb movement and firm attachment for noise reduction could be retained. The muscle activity was measured in real time using an EMG device (SYNA ACT, MT11, NEC, Japan) and a laptop computer with programme Vital Recorder II (Kissei Comtec, Japan). The raw EMG data were recorded at a sensitivity of 1000 mn and sampling frequency of 1 kHz. The time constant and high frequency filter were at 0.03 s and 100 Hz, respectively. The raw data were further filtered and analysed using programme KineAnalyzer (Kissei Comtec, Japan). This programme filtered the raw data (full wave) at low-pass 20 Hz and notch 60 Hz (the alternating current in Western Japan is 60 Hz) for better flattening of the baselines. In this study, the tapping action was defined according to tapping pressure waves, which refers to the range from the point at which a pressure wave begins to the point at which the next wave begins. In the data analysis, iEMG (integrated EMG), contraction time and iEMG/s were selected as the indexes of muscle activity. The iEMG (mV·s) is the absolute mathematical integral of the raw EMG signal within contraction time, and contraction time means the time (seconds) that a thumb muscle contracts in each tapping action. Then iEMG/s (mV) is the absolute mathematical integral divided by contraction time. Figure 4 shows as an example that how the raw EMG data are processed. The values of iEMG, contraction time and iEMG/s were calculated for each tapping action, and then averaged. In order to avoid individual bias, the EMG comparisons are presented by percentage of reference value. The 50th percentiles of EMG values (iEMG, contraction time and iEMG/s) were first calculated as reference value for each participant in a task, and then the EMG values were divided by the 50th percentiles to calculate the individual percentage of reference value. The final percentage of reference value (y-axis in Figures 6 – 8) was the mean value across all the participants with variability of standard error.
Figure 4. Example of EMG signal processing: A– B means a muscle contraction in a tapping action, iEMG (mV·s): the absolute mathematical integral between A and B; contraction time (second): the duration of muscle contraction between A and B.; iEMG/s (mV): mV·s/contraction time.
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Table 2.
Comparison of fatigue time and tapping speed between tasks in three tasks (n ¼ 20). Tapping task
Fatigue time Fixed speed (second) Max speed Tapping speed (taps/minute)
Moving task
Circling task
Large
Small
Adduction – abduction
Flexion – extension
Clockwise
Counterclockwise
193.7 ^ 46.5 57.9 ^ 30.7 213 ^ 65.1
132.4 ^ 43.7** 46.3 ^ 29.1* 223 ^ 71.2
118.6 ^ 48.4 41.9 ^ 30.5 168 ^ 66.4
124.2 ^ 48.3 48.3 ^ 30.2 132 ^ 44.3**
126.9 ^ 45.5 56.3 ^ 27.2 144 ^ 61.2
110.3 ^ 44.6 50.9 ^ 30.1 141 ^ 70.5
Note: Values indicate means ^ SD. t-test: *p , 0.05, **p , 0.01.
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2.7.
Statistics
The differences in thumb performance (fatigue time and tapping speed) and perceived exertion evaluation between tasks were analysed using paired t-tests. The differences in the actual values of muscle activity indexes (iEMG/s, contraction time and iEMG) between tasks were analysed using Wilcoxon signed-rank tests. The results obtained in the perceived exertion evaluation were analysed by repeated two-way analysis of variance (ANOVA) to examine the influences of tasks and muscles. All tests were conducted using IBM SPSS Statistics Version 20.0.0 (Japanese-language package). Statistical significance was accepted at p-values less than 0.05. 3.
Results
3.1. Tapping task 3.1.1. Thumb performance In both fixed and max speed subtasks, the fatigue time for the small button task was significantly shorter than that for the large button task, whereas no significant difference was found in tapping speed (Table 2). 3.1.2. Perceived exertion evaluation Significant main effects were detected in both button size and muscles upon analysis by two-way ANOVA (Figure 5). In addition, the rating of FDI was significantly higher in the small button task than in the large button task, but other thumb muscles did not exhibit any significant changes (Figure 4). 3.1.3.
EMG
The fixed speed subtask revealed that, from the large button task to the small button task, the iEMG and contraction time of FDI significantly increased, while the iEMG and iEMG/s of APB significantly decreased (Figure 6). In the max speed subtask, however, all indexes of iEMG, iEMG/s and contraction time of FDI significantly increased, whereas those of APB significantly decreased (Figure 6). 3.2.
Moving task
3.2.1. Thumb performance In terms of fatigue time, no significant difference was found in both fixed and max speed subtasks between adduction– abduction and flexion –extension tasks (Table 2). As for tapping speed, it was found that participants tapped significantly more slowly in the flexion –extension task than in the adduction – abduction task (Table 2). 3.2.2.
Perceived exertion evaluation
A significant main effect of muscle was detected, even though no significant result was obtained for orientation (Figure 5). However, a significant interaction of muscle and orientation was found (Figure 5). In addition, the ratings of APB and APL significantly decreased from the adduction– abduction to the flexion– extension task, while that of FDI significantly increased (Figure 5).
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Figure 5. Perceived exertion evaluation in three tasks (n ¼ 20). Note: Values indicate means þ SD.
3.2.3. EMG In both fixed and max speed subtasks, all indexes of iEMG, iEMG/s and contraction time of APB significantly decreased, whereas that of FDI significantly increased from the adduction – abduction to the flexion – extension task (Figure 7). 3.3.
Circling task
3.3.1. Thumb performance Both fatigue time and tapping speed did not exhibit any significant difference (Table 2). 3.3.2. Perceived exertion evaluation Two-way ANOVA analysis revealed a significant main effect of muscles, but no significant effects were found in direction and interaction of direction and muscles (Figure 5). 3.3.3. EMG None of the EMG indexes showed any statistically suggestive results (Figure 8). 4. Discussion In the perceived exertion evaluation through all the three tasks (tapping, moving and circling tasks), the results of APB, APL and FDI constantly obtained higher ratings than AP, FPB and ED, and no significant change was found in the perceived exertion evaluation of AP, FPB and ED. Thus, the analysis focused on APB, APL and FDI, in which the most significant variations occurred. Furthermore, among APB, APL and FDI, it is seen that the ratings of perceived exertion evaluation in APL and APB varied in a same tendency, but differed from those of FDI. It has been found that APL and APB function in an interdependent manner, and APB is more active than APL (Brandsma, Oudenaarde, and Oostendorp 1996). Barandun et al. (2009) also stated that APB is major importance for the thenar muscles. Moreover, since no statistically suggestive finding was found in the EMG measures of APL and ED, thus the EMG results of these two muscles were excluded from the data presentation. The discussions focus the comparison only between APB and FDI. 4.1.
Tapping task
In the results of thumb performance, it was seen that the thumb developed fatigue more rapidly for the small button task than for the large button task, whereas no significant difference was found in tapping speed (Table 2). In the evaluation of
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Figure 6. EMG comparison of FDI and APB in tapping task (n ¼ 20). Note: Values indicate means þ SE.
perceived exertion, the FDI was the only muscle that was rated as having greater perceived exertion when the thumb shifted from the large button to the small button task. This matches the EMG results that the muscle activity of FDI significantly increased from the large button to the small button task in both fixed and max speed subtasks (Figure 6). This could be explained from a tapping posture perspective. During the experiment, it was observed that the thumb posture of participants was likely to be more vertical in the small button task than in the larger button task (Figure 9). In order to tap a smaller button, the contact area of the thumb tip on the screen needs to be reduced. Thus, the tapping posture of the thumb becomes vertical rather than oblique, so that the contact area of the thumb tip could be reduced. This assumption is supported by a previous study finding that vertical thumb posture was commonly applied among participants when touching smaller keys on a smartphone touch screen, since this posture has a smaller thumb tip contact area that could maintain accuracy in target selection (Park and Han 2010). As a smaller button is physically harder to locate, in order to maintain stability of the vertical posture and phone mockup while ensuring the accuracy of tapping, the grip force is required to be increased and more precise. In addition, the index finger tends to be the primary grip finger while the phone is being held in the first web space (Figure 3). It has been found that the muscle activity of FDI became greater when the thumb and index finger performed a precision grip compared with a sole power grip (Anson et al. 2002), and FDI is a muscle in the thumb that provides distally and ulnarly directed force for the first metacarpal bone (Brand and Hollister 1993). In addition, a previous study also found that, while thumb and index finger were performing a precision grip, the muscle activity of FDI increased with a reduced width of the object being gripped (Hasegawa et al. 2001). Thus, it is suggested that the FDI increases muscle effort to match the increased workload demand in performing the small button task.
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Figure 7. EMG comparison of FDI and APB in moving task (n ¼ 20). Note: Values indicate means þ SE.
On the other hand, it was also found that a significant decrease of muscle activity occurred in APB. As Figure 5 shows, the iEMG and iEMG/s of APB significantly decreased when the thumb shifted from the large button to the small button task. In other words, the muscle effort of APB decreased in the small button task. As such, it could be assumed that the general tolerance of the thumb subjected to fatigue should not differ between these two tasks. However, the thumb tends to exhibit an oblique posture when operating a large button (Park and Han 2010), as observed during the large button task. Meanwhile, as the target button becomes larger, the contact area between the thumb tip and the touch screen increases, so the thumb performs power pushing instead. This introduces the thumb and other fingers to performance thenar eminence grip in every push. Thus, the muscle effort of APB as one of the thenar eminence muscles increases. When the thumb shifts to a vertical posture in the small button task, maintenance of a vertical posture becomes more necessary for performing the task, since the thump tip contact area is required to be reduced. As a result, the thenar eminence grip is reduced, so the muscle effort from APB decreases.
4.2.
Moving task
According to the results of thumb performance, the adduction – abduction task shows faster tapping speed than the flexion– extension task (Table 2). This result is consistent with a previous study that found that the thumb motor performance on a mobile was better (thumb moves faster) in ‘outward’ (adduction – abduction) movements than in ‘inward’ (flexion– extension) movements (Trudeau et al. 2011), and also matches another study finding that thumb motor performance decreased at both the bottom right and the top left corners of a touch screen mobile phone (Trudeau et al. 2012). During the experiment, it was observed that, when the thumb was moving from the distal top left corner (Figure 10A) to the proximal
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Figure 8. EMG comparison of FDI and APB in circling task (n ¼ 20). Note: Values indicate means þ SE.
bottom right corner (Figure 10B) of the flexion– extension task, the thumb adopted a vertical posture (Figure 10C). As the contact area between the thumb tip and touch screen is reduced, the grip force becomes more precise in order to maintain the accuracy of target selection (Park and Han 2010). It has been found that the muscle effort of FDI tends to increase with increasing precision grip in the last tapping task. Thus, the increased muscle activity of FDI in the flexion –extension task is explained (Figure 7). This matches the results of perceived exertion evaluation that FDI is the only muscle for which increased perceived exertion was rated in the flexion– extension task. Furthermore, as FDI was involved more and contracted longer (Figure 7) for maintaining a vertical thumb posture in the flexion– extension task, the tapping speed was reduced as a result.
Figure 9.
Tapping postures in tapping task (left: large button, right: small button).
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Figure 10. Moving postures in flexion – extension task (A: distant extension corner, B: proximal flexion corner, C: grip posture of proximal flexion corner).
However, the results of thumb performance also show that there is no significant difference in the fatigue time between these two tasks (Table 2). In addition, it is also seen that the muscle effort of APB appeared to be decreased in the flexion– extension task (Figure 6). According to Figure 6, all indexes of iEMG, contraction time and iEMG/s in APB are significantly higher in the adduction–abduction task than in the flexion–extension task (both fixed and max speed subtasks). Throughout the adduction–abduction task, it was observed that the thumb constantly maintained an oblique posture (Figure 11C) at both the upper-right adduction corner (Figure 11A) and the bottom-left abduction corner (Figure 11B). This posture is highly similar to the one that was observed in the large button task of the tapping task (Figure 8). As it has been discussed above that an oblique thumb posture tends to lead to powerful pushing on the touch screen, the muscle effort of APB increases as a consequence. These results also matched a previous finding that APB is an abductor at the MCP and CMC joints (Gupta and Michelsen-Jost 2012), which brings about palmar and radial abduction in the thumb (Schmidt and Lanz 2004). Thus, combined with the above findings about FDI, the results of this task suggest that, when the thumb is operating a smartphone touch screen, the demand on APB is increased for moving in an adduction–abduction orientation, whereas that on FDI is increased for flexion–extension. This matches the perceived exertion evaluation that FDI received a higher perceived exertion rating in the flexion–extension task, whereas the rating for APB was higher in the adduction–abduction task. Owing to this pattern of contribution in muscle effort among the thumb muscles, the total development of subjective fatigue tended to be even between these two tasks. This is considered as the reason why the fatigue time did not significantly vary. In other words, moving the thumb in adduction–abduction is not likely to improve the thumb’s susceptibility to fatigue compared with that in the flexion–extension orientation, even if it could induce the thumb to move faster. 4.3. Circling task In this task, no statistically suggestive finding was obtained. In this circling task, the participants were asked to tap each of the four end corners of the keyboard area of the phone mock-up in these two different directions of clockwise and counterclockwise. Thus, the thumb performed even more complicated movements than it did in previous tasks, since both tapping and reaching the ends of the two moving orientations were involved in a task. Moreover, according to Trudeau et al. (2011), a north-east –south-west (clockwise) direction showed better thumb motor performance than all other orientations. This led to an expectation that greater variations in the muscle activity would occur in this circle task. However, no significant difference was found in fatigue time and tapping speed (Table 2), EMG (Figure 8), as well as in perceived exertion evaluation (Figure 4) when comparing between these two circling directions. This shows that circling direction is not a factor that affects the thumb performance when using a smartphone touch screen with one hand. 4.4. Limitations The limitations of this study include that the thumb muscles AP and FBP were not included in the EMG assessment, although they were in the perceived exertion evaluation. Owing to the limited space between the thumb muscles and
Figure 11. Moving postures in adduction –abduction task (A: up-right adduction corner, B: bottom-left abduction corner, C: grip posture of adduction – abduction task).
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smartphone mock-up, it was impractical to place EMG electrodes on these two muscles. In addition, such placement would have greatly affected the thumb movements. According to Schmidt and Lanz (2004), AP is the largest but deepest thenar muscle and FPB is an important flexor in the thumb. Thus, the exclusion of these two muscles may have caused the muscle workload of APB, FDI and APL to be overestimated. However, in the perceived exertion evaluation, AP and FBP constantly obtained lower ratings than the other four muscles and did not show any significant variations when the tasks were changed (Figure 1). While the phone mock-up was being held in the hand, its right corner remained at the position of AP and FBP all the time during the movements of these two muscles. This could have caused these two muscles not to be significantly involved in the thumb operating tasks of the experiments, and their perceived exertions did not significantly vary as a result. In addition, the positions of AP, FBP and APB are very close, which may potentially bias the participants in terms of distinguishing the perceived exertion among these muscles. According to Gupta and Michelsen-Jost (2012), APB is the most superficial thenar muscle (this means that the movement of APB is not as likely to be affected by the phone mock-up as AP and FBP) and innervated by the median nerve. However, most of the muscle fibres of AP are united with the tendons of FBP, and these two muscles are innervated by the ulnar nerve (Gupta and Michelsen-Jost 2012). This implies that the participants are very likely to be able to differentiate APB from AP and FBP, even though their ability to distinguish between AP and FBP may be limited. However, as AP and FBP were not significantly involved in the operating tasks, it was believed that the exclusion of these two muscles did not bias the results of the study. Moreover, this study only tested the keyboard area of the touch screen. Movements on other areas of the touch screen (such as the upper area) that may require different muscles in the thumb, hand, forearm and shoulder were not covered. Therefore, different choices of the testing area and muscles may have shown other differences in the muscle workload, for which the explanations could have been different. 5. Conclusions This study shows that button size is a factor affecting thumb performance in the operation of smartphone touch screens. The muscle effort and perceived exertion of FDI increase when the thumb is tapping smaller buttons (diameter: 3 mm), and this is regarded as the reason why the thumb develops fatigue more rapidly than with larger buttons (diameter: 9 mm). While the thumb is moving in a flexion –extension orientation, the muscle effort and perceived exertion of FDI increase. Meanwhile, the muscle effort and perceived exertion of APB increase when the thumb is moving in an adduction – abduction orientation. This suggests that FDI is placed under greater demands in flexion – extension, whereas APB is in adduction– abduction orientation task. Moreover, as no statistically suggestive results were found in the circling task, it is suggested that circling direction is not a factor affecting thumb operation of a smartphone touch screen. All in all, this study suggests that, in the design of hand-held device interfaces, the use of small buttons should be minimised to reduce the effort-related demands on FDI, which could cause the thumb to be less susceptible to fatigue. References Anson, J. G., Y. Hasegawa, T. Kasai, M. L. Latash, and S. Yahagi. 2002. “EMG Discharge Patterns during Human Grip Movement are Task-Dependent and Not Modulated by Muscle Contraction Modes: A Transcranial Magnetic Stimulation (SMS) Study.” Brain Research 934: 162– 166. Barandun, M., V. V. Tscharner, C. Meuli-Simmen, V. Bowen, and V. Valderrabano. 2009. “Frequency and Conduction Velocity Analysis of the Abductor Pollicis Brevis Muscle during Early Fatigue.” Journal of Electromyography and Kinesiology 19: 65– 74. Berolo, S., R. P. Wells, and B. C. Amik III. 2011. “Musculoskeletal Symptoms among Mobile Hand-Held Device Users and Their Relationship to Device Use: A Preliminary Study in a Canadian University Population.” Applied Ergonomics 42: 371– 378. Brand, P. W., and A. Hollister, eds. 1993. “Mechanics of Individual Muscles at Individual Joints.” In Clinical Mechanics of the Hand, 254– 352. St Louis, MO: Mosby Press. Brandsma, J. W., E. Oudenaarde, and R. Oostendorp. 1996. “The Abductores Pollicis Muscles. Clinical Consideration Based on Electromyographical and Anatomical Studies.” Journal of Hand Therapy 9: 218– 222. Goggin, G. 2006. Cell Phone Culture: Mobile Technology in Everyday Life. London: Routledge. Gupta, S., and H. Michelsen-Jost. 2012. “Anatomy and Function of the Thenar Muscles.” Hand Clinics 28 (1): 1 – 7. Gustafsson, E., P. W. Johnson, A. Lindegard, and M. Hagberg. 2011. “Technique, Muscle Activity and Kinematic Differences in Young Adults Texting on Mobile Phones.” Ergonomics 54 (5): 477– 487. Hasegawa, Y., T. Kasai, H. Kinoshita, and S. Yahagi. 2001. “Modulation of a Motor Evoked Response to Transcranial Magnetic Stimulation by the Activity Level of the First Dorsal Interosseous Muscle in Humans When Grasping a Stationary Object with Different Grip Widths.” Neuroscience Letters 299 (1– 2): 1 – 4. Hogg, N. A. 2010. “Design of Thumb Keyboard: Performance, Effort and Kinematics.” Master Thesis, University of Waterloo, Waterloo, Canada. International Telecommunication Union. 2012. Measuring the Information Society, http://www.itu.int/en/ITU-D/Statistics/Documents/ publications/mis2012/MIS2012_without_Annex_4.pdf Jonsson, P., P. W. Johnson, M. Hagberg, and M. Forsman. 2011. “Thumb Joint Movement and Muscular Activity during Mobile Phone Texting – A Methodological Study.” Journal of Electromyography and Kinesiology 21: 363–370.
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Mamaghani, N. K., Y. Shimorura, K. Iwanaga, and T. Katsuura. 2009. “Effects of Strap Support in a Hand-Held Device on the Muscular Activity in Female Worker Assessed by Electromyography and Subjective Rating.” Ergonomics 52 (7): 848– 859. McGorry, R. W., J. H. Lin, P. G. Dempsey, and J. S. Casey. 2010. “Accuracy of the Borg CR10 Scale for Estimating Grip Forces Associated with Hand Tool Tasks.” Journal of Occupational and Environmental Hygiene 7: 298– 306. Park, Y. S., and S. H. Han. 2010. “One-Handed Thumb Interaction of Mobile Devices from the Input Accuracy Perspective.” International Journal of Industrial Ergonomics 40: 746– 756. Schmidt, H., and U. Lanz. 2004. Surgical Anatomy of the Hand. New York: Thieme. Seror, P., T. Maisonobe, and P. Bouche. 2011. “A New Electrode Placement for Recording the Compound Motor Action Potential of the First Dorsal Interosseous Muscle.” Clinical Neurophysiology 41: 173– 180. Trudeau, M. B., T. Udtamadilok, A. K. Karlson, and J. K. Dennerlein. 20122012. “Thumb Motor Performance Varies by Movement Orientation, Direction, and Device Size during Single-Handed Mobile Phone Use.” The Journal of Human Factors and Ergonomics Society 54: 52 – 59. Trudeau, M. B., J. G. Young, D. L. Jindrich, and J. K. Dennerlein. 2011. “Thumb Motor Performance Varies with Thumb and Wrist Posture during Single-Handed Mobile Phone Use.” Journal of Biomechanics 45: 2349– 2354. UN News Service. 2008. Number of Cell Phone Subscribers to Hit 4 Billion this Year, UN Says, http://www.un.org/apps/news/story.asp? NewsID¼28251#.UgGl6tLvjTo Wos, H., C. Noworl, T. Marek, and G. Borg. 1988. “The Reliability of Self-Ratings Based on Borg’s Scale for Hand-Arm Vibrations of Short Duration (Part II).” International Journal of Industrial Ergonomics 2: 151– 156.
Ergonomics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/terg20
An ergonomics study of thumb movements on smartphone touch screen a
Jinghong Xiong & Satoshi Muraki a
b
Graduate School of Design, Kyushu University, Fukuoka, Japan
b
Faculty of Design, Kyushu University, Fukuoka, Japan Published online: 08 Apr 2014.
Click for updates To cite this article: Jinghong Xiong & Satoshi Muraki (2014) An ergonomics study of thumb movements on smartphone touch screen, Ergonomics, 57:6, 943-955, DOI: 10.1080/00140139.2014.904007 To link to this article: http://dx.doi.org/10.1080/00140139.2014.904007
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Ergonomics, 2014 Vol. 57, No. 6, 943–955, http://dx.doi.org/10.1080/00140139.2014.904007
An ergonomics study of thumb movements on smartphone touch screen Jinghong Xionga* and Satoshi Murakib a
Graduate School of Design, Kyushu University, Fukuoka, Japan; bFaculty of Design, Kyushu University, Fukuoka, Japan
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(Received 8 April 2013; accepted 28 February 2014) This study investigated the relationships between thumb muscle activity and thumb operating tasks on a smartphone touch screen with one-hand posture. Six muscles in the right thumb and forearm were targeted in this study, namely adductor pollicis, flexor pollicis brevis, abductor pollicis brevis (APB), abductor pollicis longus, first dorsal interosseous (FDI) and extensor digitorum. The performance measures showed that the thumb developed fatigue rapidly when tapping on smaller buttons (diameter: 9 mm compared with 3 mm), and moved more slowly in flexion – extension than in adduction– abduction orientation. Meanwhile, the electromyography and perceived exertion values of FDI significantly increased in small button and flexion– extension tasks, while those of APB were greater in the adduction– abduction task. This study reveals that muscle effort among thumb muscles on a touch screen smartphone varies according to the task, and suggests that the use of small touch buttons should be minimised for better thumb performance. Practitioner Summary: Through measurements of electromyography, thumb performance and perceived exertion, this study reveals that demand on FDI increases when the thumb taps small buttons and in flexion– extension orientation, but that demand on APB increases in adduction –abduction orientation. The decreased thumb performance with small buttons is attributed to a combination of smaller targets and increased demand on FDI. Keywords: smartphone touch screen; thumb performance; EMG; perceived exertion
1.
Introduction
Mobile phones are now such a significant communication tool that they play an irreplaceable role in our everyday life and work. In the 10 years between 1995 and 2004, the number of mobile phone users worldwide increased from 91 million to approximately 1.75 billion (Goggin 2006). In 2008, this figure jumped up to about 4 billion (UN News Service 2008) and reached 6 billion by 2012 (International Telecommunication Union 2012). Since touch screens were introduced into mobile phones a few years ago and have since become increasingly affordable, it is believed that touch screen mobile phones will include even more users in the future. Although touch screens clearly enhance the user’s experience by offering an even more user-friendly interface than tactile keypads, there is also a need to improve the input performance for the use, such as keyboard typing. In most of the cases, when holding and typing a touch screen smartphone with one hand, the thumb is likely to undertake nearly all the operating tasks. In other words, the thumb greatly affects, even determines, the input performance on the touch screen of smartphone. To date, there have been insufficient studies regarding thumb muscles in mobile use as applied to touch screen smartphones. Trudeau et al. (2012) suggested that thumb movement in adduction– adduction is faster than in flexion– extension orientation, but their explanations for such faster movement were based on thumb motor performance and joint coordination, and thumb muscle effort was barely involved. A study found that the thumb of the right hand was the most prevalent reported source of muscle pain among users of mobile hand-held devices (Berolo, Wells, and Amik 2011), but this study did not explore the reasons for the pain, and muscle effort assessment was not covered in the study. Another study found that people enter text on mobile phones with tactile keyboards by one thumb rather than two hands, and higher muscle activities were detected in the muscle abductor pollicis longus (APL) of the thumb when typing with a faster speed (Gustafsson et al. 2011). Jonsson et al. (2011) pointed out that the muscle activity of the thumb is an ideal indicator for assessing musculoskeletal loads of mobile phone use. Moreover, Hogg (2010) reported that greater effort was perceived for the thumb when using the keypad in the right bottom corner of a mobile phone, but it was not pointed out that which thumb muscles are responsible for the increased effort. However, the thumb movements on a smartphone touch screen could greatly differ from those on mobile phone with tactile keypads (e.g. key pushing is replaced by light tapping on a flat touch screen surface). Thus, whether or not these results would match those for the use of a smartphone touch screen remains unclear.
*Corresponding author. Email: [email protected] q 2014 Taylor & Francis
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This study aimed to shed light on thumb performance in smartphone touch screen operation. By referring to the variation of muscle activity and perceived exertion evaluation in the targeted thumb muscles at three categories of thumb operating task, namely tapping (large and small buttons), moving (adduction – abduction and flexion –extension orientations) and circling (clockwise and counter-clockwise directions), this study should provide a better understanding of thumb muscle activity and its connection to thumb movements on a smartphone touch screen, and offer a knowledge base for the better design of user interfaces for touch screen smartphones, as well as for other hand-held touch screen devices.
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2.
Methods
2.1. Participants A total of 20 university students (10 males, 10 females, mean (^ standard deviation) age 24.5 ^ 2.2 years) were recruited in this study. All participants were right-handed, and they all owned touch screen smartphones for daily use, such as sending emails and posting comments on social network websites. None of the participants reported a musculoskeletal disorder or pain, nor any motor disorders or symptoms. This study was approved by the Institutional Ethics Review Board of Kyushu University, Japan, and informed consent was received from each participant.
2.2. Targeted thumb muscles Six muscles in the right thumb and forearm were targeted in this study (Figure 1), namely adductor pollicis (AP), flexor pollicis brevis (FPB), abductor pollicis brevis (APB), APL, first dorsal interosseous (FDI) and extensor digitorum (ED). The position of each muscle was marked and clearly described to the participants in the experiment.
2.3.
Experiment smartphone mock-up
The smartphone mock-up used in the experiment was a physical copy of iPhone4, a global touch screen smartphone that all of the participants claimed to be familiar with. Its dimensions were 115.2 £ 58.5 £ 9.3 mm, with a weight of 140.0 g. The tested keyboard layout also copied that from iPhone4, which was 50.0 £ 33.0 mm in size, with 20.0 mm from the bottom aligned to the centre line.
2.4.
Protocol
All participants sat comfortably in an armless chair (height was adjustable to match body height) in front of a 70-cm-high table. The participants were required to place their tested right arm on the table in a posture and position that would provide acceptable comfort (Figure 2). Thus, the arm and wrist were fully supported so that the participants were able to focus their concentration on the thumb while performing the experimental tasks. The participants were asked to hold the smartphone mock-up in a posture matching that which they normally use on a daily basis. In addition, they were allowed to shift the holding posture during the experiment to regain a comfortable position, as long as the arm and wrist remained fully supported on the table. The experiment was carried out in an indoor human kinetics laboratory, where temperature and lighting were controlled in order to provide optimal experimental conditions.
Figure 1. The six targeted thumb muscles: (1) AP – adductor pollicis, (2) FPB – flexor pollicis brevis, (3) APB – abductor pollicis brevis, (4) APL – abductor pollicis longus, (5) FDI – first dorsal interosseous, (6) ED – extensor digitorum.
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Figure 2.
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The arm and phone mock-up holding posture during the experiment.
2.5. Tasks The experiment required participants to tap round and flat buttons on the keyboard layout by moving their thumb. Participants started each subtask when a signal was given, and then stopped freely when they started to perceive fatigue in the thumb (Table 1). In order to represent the overall thumb movement in this study, the experiment was divided into three tasks, namely tapping, moving and circling tasks. Each task contained two tasks, which were ‘large button’ and ‘small button’ in the tapping task, ‘adduction – abduction’ and ‘flexion –extension’ in the moving task and ‘clockwise’ and ‘counter-clockwise’ in the circling task. Furthermore, each task was composed of two subtasks, namely ‘fixed speed’ and ‘max speed’ subtasks. In the fixed speed subtask, the participants performed one tap per second coordinated with a digital metronome. The frequency of the metronome was one hertz, and all participants claimed that its sound was clear during the experiment. In the max speed subtask, the participants were asked to tap the buttons as fast as possible. The rationale for applying these two speed subtasks is as follows. First, it was to make sure that each participant was subjected to a sufficient workload to develop a feeling of fatigue. Second, if no significant difference was found in fatigue time in the max speed subtask, then tapping speed can be regarded to represent thumb performance. In addition, a rest period (at least five minutes) was provided for the participants when a subtask was completed. The next subtask was started only when the participants claimed that the fatigue feeling from the previous subtask was clear. The buttons were white, printed on a black keyboard layout, whereas the rest of the screen surface remained white. Thus, the participants could clearly see the layout and buttons. The order of the tasks started with tapping, moving and then the circling task. However, the orders between large and small button tasks, adduction – abduction and flexion –extension tasks, as well as clockwise and counter-clockwise tasks were randomised. The order of subtasks always started with a fixed speed, and finished with the max speed subtask. 2.5.1.
Tapping task
The tapping task required participants simply to tap a round paper button (with a slim pressure sensor underneath) in the physical centre of the keyboard layout (Figure 3A). In the large button task, participants tapped a button with a diameter of
Table 1.
Experiment outline. Large button
Tapping Small button Adduction – abduction Moving Flexion – extension Clockwise Circling Counter-clockwise
Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed Fixed speed Max speed
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Figure 3. Tasks in three tasks: A. Tapping task: large button task – button diameters of 9.0 mm, small button task – 3.0 mm; B. Moving task: adduction – abduction task – orientation between a and c, flexion– extension task – b and d; C. Circling task: clockwise task – direction a-b-c-d, counter-clockwise task: a-d-c-b.
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9.0 mm; in the small button task, the button’s size was reduced to 3.0 mm. The distance between the target buttons (physical centre) was 15.0 mm for large button task and 5.0 mm for small button task. 2.5.2. Moving task Two tasks were included, namely adduction –abduction and flexion– extension task. In adduction –abduction task, participants tapped between the two round buttons (pressure sensors with diameter 9.0 mm) at the two end adduction– abduction orientation corners on the keyboard layout, whereas they tapped the other two end corners of flexion –extension orientation in flexion –extension task (Figure 3B). The button size was chosen at 9.0 mm in diameter to avoid the bias of tapping a small button, since it is physically harder than a large button to locate. The distance between the target buttons (physical centre) was 60.0 mm for both orientations (a – c for adduction – abduction and b– d for flexion –extension, Figure 3B). 2.5.3.
Circling task
Participants undertook two tasks, namely clockwise and counter-clockwise task. Participants tapped the four round buttons (pressure sensors with diameter 9.0 mm) at each end corner of the keyboard layout (Figure 3C). In clockwise task, the tapping direction was from top-right, down-right, down-left and then to top-left; in counter-clockwise task, the process still started at the top-right button but the direction was opposite. The distance between the target buttons (physical centre) of top-right and down-right, top-left and down-left was 33.0 mm (the width of the keyboard layout, Figure 3C), and that of topright and top-left, down-right and down-left was 50.0 mm (the length of the keyboard layout, Figure 3C). 2.6. 2.6.1.
Measurements Thumb performance
2.6.1.1. Fatigue time. The time at which participants started tapping (when the start signal was given) until they stopped was defined as the ‘fatigue time’ in this study. The time of stopping was defined as the point at which they started to perceive a feeling of fatigue in the thumb leading them to stop tapping. A shorter fatigue time indicates that the thumb becomes more uncomfortable since it develops fatigue more rapidly. 2.6.1.2 Tapping speed. Tapping speed refers to how fast the participants could tap a button in the max speed subtasks. An extremely thin pressure sensor was firmly attached (model: FlexiForce A201-1, Nitta, Japan) underneath each button and connected with the EMG recording laptop to record the tapping pressure waves and count the taps. 2.6.2. Perceived exertion evaluation All six targeted thumb muscles were included in this assessment. After each task was completed, the participants immediately filled out a Borg’s CR-10 scale form to rate their perceived level of fatigue in each targeted thumb muscle. Participants marked their perceived level of fatigue from individual number 0 –10 in the scale for each targeted muscle, while 0 stands for no feeling of fatigue and 10 stands for the maximum level. Borg’s scale is a widely used tool to assess the degree of subjective feeling of fatigue (Mamaghani et al. 2009). Wos et al. (1988) stated that Borg’s scale is highly reliable in the evaluation of hand-arm vibrations of short duration. Moreover, a study found that Borg’s CR-10 scale was highly reliable in the evaluation of subjective muscle fatigue in the radial side of the hand (thumb), when the hand was performing grip force (McGorry et al. 2010). Even though they did not describe that this tool has been used for assessing thumb
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muscles, the above studies strongly support the assertion that this tool is reliable for this study. In addition, the experiment was carefully set up. The arm holding the phone mock-up was fully supported (including the wrist) by putting it on the table with a comfortable posture, and the positions of the muscles were clearly marked and explained by instructions to the participants. When a muscle was marked, the participants were asked to tap on the phone mock-up and inform the researchers whether or not they had understood the position of the muscle. Furthermore, after all the muscles were marked, the participants were asked to tap on the phone mock-up to compare their recognition to the positions of the marked muscles, and their perception of the effort that these muscles made when tapping on the phone mock-up. Before the tasks started, all the participants claimed that they had understood the positions of the marked muscles and what to do in terms of rating their perceived exertion for the muscles. Furthermore, in order to provide a positive physical environment for the participants to maintain their concentration on the tasks, the temperature and other environmental factors were also well controlled throughout the experiment.
2.6.3. Electromyography (EMG) Four muscles, namely APB, APL, FDI and ED, were registered for EMG assessment. In APB, the electrodes were placed over the muscle belly between the metacarpophalangeal (MCP I) and carpometacarpal (CMC I) joints (Seror, Maisonobe, and Bouche 2011), while the electrodes for APL were placed on the forearm proximal to the styloid process of the radius (Gustafsson et al. 2011). In FDI, the electrodes were placed over the muscle belly above the base of the thumb and proximal to the base of the index finger, and the electrodes for ED were one-third of the distance between the epicondyle and the styloid process of the radius (Gustafsson et al. 2011). All electrodes were covered by adhesive sports bandages, so that both flexibility for thumb movement and firm attachment for noise reduction could be retained. The muscle activity was measured in real time using an EMG device (SYNA ACT, MT11, NEC, Japan) and a laptop computer with programme Vital Recorder II (Kissei Comtec, Japan). The raw EMG data were recorded at a sensitivity of 1000 mn and sampling frequency of 1 kHz. The time constant and high frequency filter were at 0.03 s and 100 Hz, respectively. The raw data were further filtered and analysed using programme KineAnalyzer (Kissei Comtec, Japan). This programme filtered the raw data (full wave) at low-pass 20 Hz and notch 60 Hz (the alternating current in Western Japan is 60 Hz) for better flattening of the baselines. In this study, the tapping action was defined according to tapping pressure waves, which refers to the range from the point at which a pressure wave begins to the point at which the next wave begins. In the data analysis, iEMG (integrated EMG), contraction time and iEMG/s were selected as the indexes of muscle activity. The iEMG (mV·s) is the absolute mathematical integral of the raw EMG signal within contraction time, and contraction time means the time (seconds) that a thumb muscle contracts in each tapping action. Then iEMG/s (mV) is the absolute mathematical integral divided by contraction time. Figure 4 shows as an example that how the raw EMG data are processed. The values of iEMG, contraction time and iEMG/s were calculated for each tapping action, and then averaged. In order to avoid individual bias, the EMG comparisons are presented by percentage of reference value. The 50th percentiles of EMG values (iEMG, contraction time and iEMG/s) were first calculated as reference value for each participant in a task, and then the EMG values were divided by the 50th percentiles to calculate the individual percentage of reference value. The final percentage of reference value (y-axis in Figures 6 – 8) was the mean value across all the participants with variability of standard error.
Figure 4. Example of EMG signal processing: A– B means a muscle contraction in a tapping action, iEMG (mV·s): the absolute mathematical integral between A and B; contraction time (second): the duration of muscle contraction between A and B.; iEMG/s (mV): mV·s/contraction time.
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Table 2.
Comparison of fatigue time and tapping speed between tasks in three tasks (n ¼ 20). Tapping task
Fatigue time Fixed speed (second) Max speed Tapping speed (taps/minute)
Moving task
Circling task
Large
Small
Adduction – abduction
Flexion – extension
Clockwise
Counterclockwise
193.7 ^ 46.5 57.9 ^ 30.7 213 ^ 65.1
132.4 ^ 43.7** 46.3 ^ 29.1* 223 ^ 71.2
118.6 ^ 48.4 41.9 ^ 30.5 168 ^ 66.4
124.2 ^ 48.3 48.3 ^ 30.2 132 ^ 44.3**
126.9 ^ 45.5 56.3 ^ 27.2 144 ^ 61.2
110.3 ^ 44.6 50.9 ^ 30.1 141 ^ 70.5
Note: Values indicate means ^ SD. t-test: *p , 0.05, **p , 0.01.
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2.7.
Statistics
The differences in thumb performance (fatigue time and tapping speed) and perceived exertion evaluation between tasks were analysed using paired t-tests. The differences in the actual values of muscle activity indexes (iEMG/s, contraction time and iEMG) between tasks were analysed using Wilcoxon signed-rank tests. The results obtained in the perceived exertion evaluation were analysed by repeated two-way analysis of variance (ANOVA) to examine the influences of tasks and muscles. All tests were conducted using IBM SPSS Statistics Version 20.0.0 (Japanese-language package). Statistical significance was accepted at p-values less than 0.05. 3.
Results
3.1. Tapping task 3.1.1. Thumb performance In both fixed and max speed subtasks, the fatigue time for the small button task was significantly shorter than that for the large button task, whereas no significant difference was found in tapping speed (Table 2). 3.1.2. Perceived exertion evaluation Significant main effects were detected in both button size and muscles upon analysis by two-way ANOVA (Figure 5). In addition, the rating of FDI was significantly higher in the small button task than in the large button task, but other thumb muscles did not exhibit any significant changes (Figure 4). 3.1.3.
EMG
The fixed speed subtask revealed that, from the large button task to the small button task, the iEMG and contraction time of FDI significantly increased, while the iEMG and iEMG/s of APB significantly decreased (Figure 6). In the max speed subtask, however, all indexes of iEMG, iEMG/s and contraction time of FDI significantly increased, whereas those of APB significantly decreased (Figure 6). 3.2.
Moving task
3.2.1. Thumb performance In terms of fatigue time, no significant difference was found in both fixed and max speed subtasks between adduction– abduction and flexion –extension tasks (Table 2). As for tapping speed, it was found that participants tapped significantly more slowly in the flexion –extension task than in the adduction – abduction task (Table 2). 3.2.2.
Perceived exertion evaluation
A significant main effect of muscle was detected, even though no significant result was obtained for orientation (Figure 5). However, a significant interaction of muscle and orientation was found (Figure 5). In addition, the ratings of APB and APL significantly decreased from the adduction– abduction to the flexion– extension task, while that of FDI significantly increased (Figure 5).
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Figure 5. Perceived exertion evaluation in three tasks (n ¼ 20). Note: Values indicate means þ SD.
3.2.3. EMG In both fixed and max speed subtasks, all indexes of iEMG, iEMG/s and contraction time of APB significantly decreased, whereas that of FDI significantly increased from the adduction – abduction to the flexion – extension task (Figure 7). 3.3.
Circling task
3.3.1. Thumb performance Both fatigue time and tapping speed did not exhibit any significant difference (Table 2). 3.3.2. Perceived exertion evaluation Two-way ANOVA analysis revealed a significant main effect of muscles, but no significant effects were found in direction and interaction of direction and muscles (Figure 5). 3.3.3. EMG None of the EMG indexes showed any statistically suggestive results (Figure 8). 4. Discussion In the perceived exertion evaluation through all the three tasks (tapping, moving and circling tasks), the results of APB, APL and FDI constantly obtained higher ratings than AP, FPB and ED, and no significant change was found in the perceived exertion evaluation of AP, FPB and ED. Thus, the analysis focused on APB, APL and FDI, in which the most significant variations occurred. Furthermore, among APB, APL and FDI, it is seen that the ratings of perceived exertion evaluation in APL and APB varied in a same tendency, but differed from those of FDI. It has been found that APL and APB function in an interdependent manner, and APB is more active than APL (Brandsma, Oudenaarde, and Oostendorp 1996). Barandun et al. (2009) also stated that APB is major importance for the thenar muscles. Moreover, since no statistically suggestive finding was found in the EMG measures of APL and ED, thus the EMG results of these two muscles were excluded from the data presentation. The discussions focus the comparison only between APB and FDI. 4.1.
Tapping task
In the results of thumb performance, it was seen that the thumb developed fatigue more rapidly for the small button task than for the large button task, whereas no significant difference was found in tapping speed (Table 2). In the evaluation of
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Figure 6. EMG comparison of FDI and APB in tapping task (n ¼ 20). Note: Values indicate means þ SE.
perceived exertion, the FDI was the only muscle that was rated as having greater perceived exertion when the thumb shifted from the large button to the small button task. This matches the EMG results that the muscle activity of FDI significantly increased from the large button to the small button task in both fixed and max speed subtasks (Figure 6). This could be explained from a tapping posture perspective. During the experiment, it was observed that the thumb posture of participants was likely to be more vertical in the small button task than in the larger button task (Figure 9). In order to tap a smaller button, the contact area of the thumb tip on the screen needs to be reduced. Thus, the tapping posture of the thumb becomes vertical rather than oblique, so that the contact area of the thumb tip could be reduced. This assumption is supported by a previous study finding that vertical thumb posture was commonly applied among participants when touching smaller keys on a smartphone touch screen, since this posture has a smaller thumb tip contact area that could maintain accuracy in target selection (Park and Han 2010). As a smaller button is physically harder to locate, in order to maintain stability of the vertical posture and phone mockup while ensuring the accuracy of tapping, the grip force is required to be increased and more precise. In addition, the index finger tends to be the primary grip finger while the phone is being held in the first web space (Figure 3). It has been found that the muscle activity of FDI became greater when the thumb and index finger performed a precision grip compared with a sole power grip (Anson et al. 2002), and FDI is a muscle in the thumb that provides distally and ulnarly directed force for the first metacarpal bone (Brand and Hollister 1993). In addition, a previous study also found that, while thumb and index finger were performing a precision grip, the muscle activity of FDI increased with a reduced width of the object being gripped (Hasegawa et al. 2001). Thus, it is suggested that the FDI increases muscle effort to match the increased workload demand in performing the small button task.
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Figure 7. EMG comparison of FDI and APB in moving task (n ¼ 20). Note: Values indicate means þ SE.
On the other hand, it was also found that a significant decrease of muscle activity occurred in APB. As Figure 5 shows, the iEMG and iEMG/s of APB significantly decreased when the thumb shifted from the large button to the small button task. In other words, the muscle effort of APB decreased in the small button task. As such, it could be assumed that the general tolerance of the thumb subjected to fatigue should not differ between these two tasks. However, the thumb tends to exhibit an oblique posture when operating a large button (Park and Han 2010), as observed during the large button task. Meanwhile, as the target button becomes larger, the contact area between the thumb tip and the touch screen increases, so the thumb performs power pushing instead. This introduces the thumb and other fingers to performance thenar eminence grip in every push. Thus, the muscle effort of APB as one of the thenar eminence muscles increases. When the thumb shifts to a vertical posture in the small button task, maintenance of a vertical posture becomes more necessary for performing the task, since the thump tip contact area is required to be reduced. As a result, the thenar eminence grip is reduced, so the muscle effort from APB decreases.
4.2.
Moving task
According to the results of thumb performance, the adduction – abduction task shows faster tapping speed than the flexion– extension task (Table 2). This result is consistent with a previous study that found that the thumb motor performance on a mobile was better (thumb moves faster) in ‘outward’ (adduction – abduction) movements than in ‘inward’ (flexion– extension) movements (Trudeau et al. 2011), and also matches another study finding that thumb motor performance decreased at both the bottom right and the top left corners of a touch screen mobile phone (Trudeau et al. 2012). During the experiment, it was observed that, when the thumb was moving from the distal top left corner (Figure 10A) to the proximal
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Figure 8. EMG comparison of FDI and APB in circling task (n ¼ 20). Note: Values indicate means þ SE.
bottom right corner (Figure 10B) of the flexion– extension task, the thumb adopted a vertical posture (Figure 10C). As the contact area between the thumb tip and touch screen is reduced, the grip force becomes more precise in order to maintain the accuracy of target selection (Park and Han 2010). It has been found that the muscle effort of FDI tends to increase with increasing precision grip in the last tapping task. Thus, the increased muscle activity of FDI in the flexion –extension task is explained (Figure 7). This matches the results of perceived exertion evaluation that FDI is the only muscle for which increased perceived exertion was rated in the flexion– extension task. Furthermore, as FDI was involved more and contracted longer (Figure 7) for maintaining a vertical thumb posture in the flexion– extension task, the tapping speed was reduced as a result.
Figure 9.
Tapping postures in tapping task (left: large button, right: small button).
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Figure 10. Moving postures in flexion – extension task (A: distant extension corner, B: proximal flexion corner, C: grip posture of proximal flexion corner).
However, the results of thumb performance also show that there is no significant difference in the fatigue time between these two tasks (Table 2). In addition, it is also seen that the muscle effort of APB appeared to be decreased in the flexion– extension task (Figure 6). According to Figure 6, all indexes of iEMG, contraction time and iEMG/s in APB are significantly higher in the adduction–abduction task than in the flexion–extension task (both fixed and max speed subtasks). Throughout the adduction–abduction task, it was observed that the thumb constantly maintained an oblique posture (Figure 11C) at both the upper-right adduction corner (Figure 11A) and the bottom-left abduction corner (Figure 11B). This posture is highly similar to the one that was observed in the large button task of the tapping task (Figure 8). As it has been discussed above that an oblique thumb posture tends to lead to powerful pushing on the touch screen, the muscle effort of APB increases as a consequence. These results also matched a previous finding that APB is an abductor at the MCP and CMC joints (Gupta and Michelsen-Jost 2012), which brings about palmar and radial abduction in the thumb (Schmidt and Lanz 2004). Thus, combined with the above findings about FDI, the results of this task suggest that, when the thumb is operating a smartphone touch screen, the demand on APB is increased for moving in an adduction–abduction orientation, whereas that on FDI is increased for flexion–extension. This matches the perceived exertion evaluation that FDI received a higher perceived exertion rating in the flexion–extension task, whereas the rating for APB was higher in the adduction–abduction task. Owing to this pattern of contribution in muscle effort among the thumb muscles, the total development of subjective fatigue tended to be even between these two tasks. This is considered as the reason why the fatigue time did not significantly vary. In other words, moving the thumb in adduction–abduction is not likely to improve the thumb’s susceptibility to fatigue compared with that in the flexion–extension orientation, even if it could induce the thumb to move faster. 4.3. Circling task In this task, no statistically suggestive finding was obtained. In this circling task, the participants were asked to tap each of the four end corners of the keyboard area of the phone mock-up in these two different directions of clockwise and counterclockwise. Thus, the thumb performed even more complicated movements than it did in previous tasks, since both tapping and reaching the ends of the two moving orientations were involved in a task. Moreover, according to Trudeau et al. (2011), a north-east –south-west (clockwise) direction showed better thumb motor performance than all other orientations. This led to an expectation that greater variations in the muscle activity would occur in this circle task. However, no significant difference was found in fatigue time and tapping speed (Table 2), EMG (Figure 8), as well as in perceived exertion evaluation (Figure 4) when comparing between these two circling directions. This shows that circling direction is not a factor that affects the thumb performance when using a smartphone touch screen with one hand. 4.4. Limitations The limitations of this study include that the thumb muscles AP and FBP were not included in the EMG assessment, although they were in the perceived exertion evaluation. Owing to the limited space between the thumb muscles and
Figure 11. Moving postures in adduction –abduction task (A: up-right adduction corner, B: bottom-left abduction corner, C: grip posture of adduction – abduction task).
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smartphone mock-up, it was impractical to place EMG electrodes on these two muscles. In addition, such placement would have greatly affected the thumb movements. According to Schmidt and Lanz (2004), AP is the largest but deepest thenar muscle and FPB is an important flexor in the thumb. Thus, the exclusion of these two muscles may have caused the muscle workload of APB, FDI and APL to be overestimated. However, in the perceived exertion evaluation, AP and FBP constantly obtained lower ratings than the other four muscles and did not show any significant variations when the tasks were changed (Figure 1). While the phone mock-up was being held in the hand, its right corner remained at the position of AP and FBP all the time during the movements of these two muscles. This could have caused these two muscles not to be significantly involved in the thumb operating tasks of the experiments, and their perceived exertions did not significantly vary as a result. In addition, the positions of AP, FBP and APB are very close, which may potentially bias the participants in terms of distinguishing the perceived exertion among these muscles. According to Gupta and Michelsen-Jost (2012), APB is the most superficial thenar muscle (this means that the movement of APB is not as likely to be affected by the phone mock-up as AP and FBP) and innervated by the median nerve. However, most of the muscle fibres of AP are united with the tendons of FBP, and these two muscles are innervated by the ulnar nerve (Gupta and Michelsen-Jost 2012). This implies that the participants are very likely to be able to differentiate APB from AP and FBP, even though their ability to distinguish between AP and FBP may be limited. However, as AP and FBP were not significantly involved in the operating tasks, it was believed that the exclusion of these two muscles did not bias the results of the study. Moreover, this study only tested the keyboard area of the touch screen. Movements on other areas of the touch screen (such as the upper area) that may require different muscles in the thumb, hand, forearm and shoulder were not covered. Therefore, different choices of the testing area and muscles may have shown other differences in the muscle workload, for which the explanations could have been different. 5. Conclusions This study shows that button size is a factor affecting thumb performance in the operation of smartphone touch screens. The muscle effort and perceived exertion of FDI increase when the thumb is tapping smaller buttons (diameter: 3 mm), and this is regarded as the reason why the thumb develops fatigue more rapidly than with larger buttons (diameter: 9 mm). While the thumb is moving in a flexion –extension orientation, the muscle effort and perceived exertion of FDI increase. Meanwhile, the muscle effort and perceived exertion of APB increase when the thumb is moving in an adduction – abduction orientation. This suggests that FDI is placed under greater demands in flexion – extension, whereas APB is in adduction– abduction orientation task. Moreover, as no statistically suggestive results were found in the circling task, it is suggested that circling direction is not a factor affecting thumb operation of a smartphone touch screen. All in all, this study suggests that, in the design of hand-held device interfaces, the use of small buttons should be minimised to reduce the effort-related demands on FDI, which could cause the thumb to be less susceptible to fatigue. References Anson, J. G., Y. Hasegawa, T. Kasai, M. L. Latash, and S. Yahagi. 2002. “EMG Discharge Patterns during Human Grip Movement are Task-Dependent and Not Modulated by Muscle Contraction Modes: A Transcranial Magnetic Stimulation (SMS) Study.” Brain Research 934: 162– 166. Barandun, M., V. V. Tscharner, C. Meuli-Simmen, V. Bowen, and V. Valderrabano. 2009. “Frequency and Conduction Velocity Analysis of the Abductor Pollicis Brevis Muscle during Early Fatigue.” Journal of Electromyography and Kinesiology 19: 65– 74. Berolo, S., R. P. Wells, and B. C. Amik III. 2011. “Musculoskeletal Symptoms among Mobile Hand-Held Device Users and Their Relationship to Device Use: A Preliminary Study in a Canadian University Population.” Applied Ergonomics 42: 371– 378. Brand, P. W., and A. Hollister, eds. 1993. “Mechanics of Individual Muscles at Individual Joints.” In Clinical Mechanics of the Hand, 254– 352. St Louis, MO: Mosby Press. Brandsma, J. W., E. Oudenaarde, and R. Oostendorp. 1996. “The Abductores Pollicis Muscles. Clinical Consideration Based on Electromyographical and Anatomical Studies.” Journal of Hand Therapy 9: 218– 222. Goggin, G. 2006. Cell Phone Culture: Mobile Technology in Everyday Life. London: Routledge. Gupta, S., and H. Michelsen-Jost. 2012. “Anatomy and Function of the Thenar Muscles.” Hand Clinics 28 (1): 1 – 7. Gustafsson, E., P. W. Johnson, A. Lindegard, and M. Hagberg. 2011. “Technique, Muscle Activity and Kinematic Differences in Young Adults Texting on Mobile Phones.” Ergonomics 54 (5): 477– 487. Hasegawa, Y., T. Kasai, H. Kinoshita, and S. Yahagi. 2001. “Modulation of a Motor Evoked Response to Transcranial Magnetic Stimulation by the Activity Level of the First Dorsal Interosseous Muscle in Humans When Grasping a Stationary Object with Different Grip Widths.” Neuroscience Letters 299 (1– 2): 1 – 4. Hogg, N. A. 2010. “Design of Thumb Keyboard: Performance, Effort and Kinematics.” Master Thesis, University of Waterloo, Waterloo, Canada. International Telecommunication Union. 2012. Measuring the Information Society, http://www.itu.int/en/ITU-D/Statistics/Documents/ publications/mis2012/MIS2012_without_Annex_4.pdf Jonsson, P., P. W. Johnson, M. Hagberg, and M. 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