Technology and Health Care 22 (2014) 27–36 DOI 10.3233/THC-130769 IOS Press
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Virtual reality system based on Kinect for the elderly in fall prevention W.-M. Hsieha,b , C.-C. Chenc,1 , S.-C. Wangd , S.-Y. Tana,1 , Y.-S. Hwanga , S.-C. Chene,h,1 , J.-S. Laif,1 and Y.-L. Chend,g,∗ a Department
of Electronic Engineering and Graduate Institute of Computer and Communication Engineering, National Taipei University of Technology, Taipei, Taiwan b Department of Electronic Engineering, Hwa Hsia Institute of Technology, Taipei, Taiwan c Department of Management Information System, Hwa Hsia Institute of Technology, Taipei, Taiwan d Department of Computer Science, National Taipei University of Education, Taipei, Taiwan e Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan f Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan g Department of Information Management, St. Mary’s Medicine, Nursing and Management College, Yilan, Taiwan h Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan Received 5 September 2013 Accepted 2 December 2013 Abstract. BACKGROUND: Daily life movements require balance ability. Good balance control is closely related to body stability and its development. Therefore, balance training is necessary for any age group. OBJECTIVE: This study proposes the combination of Kinect and virtual reality to build an information platform of interactive scenarios, for practice and evaluation of balance ability. Real-time monitoring of SpO2 , pulse rate, velocity and reaction time during the training process is presented for the training activities of elderly physical function. METHODS: Based on the indicators of balance ability, this information platform sets out various training activities to improve balance ability, making the supposedly tedious process fun and vivid and leading to much better training results. RESULTS: The data (SpO2 , pulse rate, velocity, reaction time) collected from this platform can be sorted and analysed, and the results used to evaluate the performance of balance training, and referenced for follow-up planning in the future. The real-time pulse rate and SpO2 measurement information indicating the training activities for the elderly to maintain physical function has a positive significance. A noninvasive and unconstrained real-time method to detect the pulse rate and SpO2 during exercises is presented. The results of balance assessment scale testing of BBS and TUG for the experimental group show that effective balance really improved. The difference between the experimental group and the control group was achieved by using paired t-test. The data were analysed by the descriptive statistics on significant level of P < 0.01. ∗
Corresponding author: Y.-L. Chen, Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, No. 134, Sec. 2, Heping E. Rd., Da-an District, Taipei City 106, Taiwan. Tel.: +886 2 2732 1104; Fax: +886 2 6639 6688; E-mail: [email protected]. 1 C.-C. Chen, S.-Y. Tan, S.-C. Chen and J.-S. Lai contributed equally to this study and should all be considered first authors. c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
CONCLUSIONS: From the training results of the participants, we know that the information platform developed for enhancing balance ability is in line with practical needs. Furthermore, the fun and interesting game-like exercises it introduces are very helpful in improving balance ability, and certainly in preventing falls. Keywords: Balance, fall, Kinect, virtual reality, information platform
1. Introduction Daily life movements such as standing, walking and jumping require balance ability. Good balance control is closely related to body stability and its development. According to medical research, people who become dizzy after taking drugs may have had their balance ability substantially affected, and are more likely to fall. This is especially the case as regards the elderly. Balancing means the ability to maintain stability in all kinds of movements and postures. Most human movements rely on the foundation of balance, and maintaining body balance is critical to human movements. Body movements are pulled by gravity; a movement can be performed only after the balancing point is achieved. Although normal people have good balance ability to deal with daily needs, dizziness caused by diseases or drugs – especially taking multiple drugs, such as sleeping pills and tranquilizers – can lead to falls. The Bureau of Health Promotion, Department of Health, points out that elderly people over the age of 65 are the group most at risk of dying from a fall. The statistics show that in 2008 there were about 125,000 patients hospitalised for fall injuries. All the evidence suggests that aging does have substantial impact on the ability of balance control [1]. This study proposes the combination of Kinect with virtual reality to build an information platform of interactive scenarios, for practices and evaluation on balance ability. Based on the indicators of balance ability, this platform sets out various training activities to improve balance ability, making the supposedly tedious process fun and vivid for much better training results. Furthermore, according to the literature, training with gaming patterns results in 30% reduction in falls. The data of this type of training, like time of use, scores and joint postures, can also be recorded and sent through the network to databases on remote computer servers. The data collected from this platform can be sorted and analysed, and the results then used to evaluate the performance of balance training, and referenced for follow-up planning in the future. In their studies on incidents of falling among older people, Robbin et al. [2] argue that abnormal gaits are to blame [2]. Daubney et al. [3] studied 50 elderly people over the age of 65, and analysed the relationship between their lower limb muscle strengths and balance. The results showed that those who fell without obvious reasons had less balance ability than those who never fell, and the lower limb muscle strength was closely related to balance ability. Therefore, the muscle strength measurement index provides a very high prediction performance [3]. The community research of Robertson et al. [4] showed that the elderly and other people who had suffered falls previously could do exercise training to reduce the incidents of falling and prevent fall injuries as well [4]. Taylor et al. [5] point out that certain programmes of exercise such as the Falls Management Exercise (FaME) and Otago Exercise (OEP) programmes are effective in returning falls patients to normal functional movement and can reduce the risk of falling by as much as 30% [5]. Belgen et al. [6] argue that individual body balance ability is related to the probability of fall incidence, and people who suffer more fall incidents have less balance abilities [6]. The research of Ledin et al. [7] also showed that the elderly undertaking a period of balance training could have their balance ability improved to prevent fall and bone fracture [7]. Cho et al. [8] highlight
W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Application
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Application (Game, Browser...etc)
Unity 3D Kinect
Unity 3D Development Interface
NB (WinOS)
KinectSDK (for Windows)
,5 (PLWWHU
Kinect SDK for Windows skeleton display
skeleton display
Fig. 1. Overall schematic diagram. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/THC-130769)
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Fig. 2. System structure diagram. (Colours are visible in the online version of the article; http://dx.doi.org/ 10.3233/THC-130769)
the effects of virtual reality balance training (VRBT) with a balance board game system on balance of chronic stroke patients. It leads to significant improvement in dynamic balance in chronic stroke patients with VRBT. VRBT is feasible and suitable for chronic stroke patients with balance deficit in clinical settings [8]. Duque et al. [8] researched a new virtual-reality system (the Balance Rehabilitation Unit [BRU]) as regards balance, falls and fear of falling in a population of community-dwelling older subjects with a known history of falls. BRU training is an effective and well-accepted intervention to improve balance, increase confidence and prevent falls in the elderly [9]. Mainly due to falls in the elderly balance function decline, falls can seriously affect their health and quality of life, so falls risk assessment tool ensure an estimate of each falls prediction performance. Clinical staff assessed by static balance, posture control capacity assessment, assessment of dynamic balance ability of the elderly and other methods to determine the risk of falls. Elderly falls through a variety of interventions can be done as prevention; the way in which motion has been shown to have the effect of reducing risk of falls. Several standard falls risk assessment tools are the Berg Balance Scale (BBS), Timed Up and Go (TUG), Four Square Step Test (FSST), etc., which are various ways to detect balance and falls prevention. In related research, it has been pointed out that the BBS for assessing balance in community-dwelling older adults is good for determining reliability and validity [10]. TUG test has a good predictive ability of falls. [11,12] FSST is a dynamic standing balance ability evaluation method [13].
2. Materials and methods Figure 1 is an overall schematic illustration, where Unity3D and Kinect SDK software tools in combination with Kinect sensors are used to show the action of the human skeleton displayed in the NB side. Figure 2 shows the system structure of this study, where the development process includes the hardware connections, signals receiving and analyses, movement identifying, etc. The hardware used is Kinect (for Windows) developed by Microsoft. The software application is developed with the Microsoft
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Fig. 3. The human body on a 20 frame locating point measured by the Kinect. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/THC-130769)
officially released Kinect SDK software tool along with Unity3D, a 3D-enabled game development software tool. The Kinect SDK tool includes all the required documents and API libraries and header files, while Unity3D is a comprehensive, 3D-game and 3D interactive tool for creativity development. Kinect is used to detect the human skeleton, and the detected 3D in-depth images are converted to the skeleton tracing system, which is able to trace up to 20 skeleton joints of the human body. The coordinates of the detected skeleton joints are then reflected in the 3D scenes developed with Kinect SDK programmes using Unity 3D’s libraries to visualise the body movements and present the visual reality simulation. Figure 3 shows how the Kinect tracks the human body on a 20 frame locating point; the captured coordinates (x, y , z ) are displayed on the screen, and the coordinate values can be used as a basis for object manipulation. The interactive multimedia system includes the Kinect, unity 3D and the proposed programme, which captures the upper or lower extremity of a subject to understand postures of the body. This study mainly focuses on fall prevention, and the proposed training and test items are designed by making references to the Four Square Step Test (FSST) method often used for examining balance ability; one of the designed activities uses the hand or foot or the combination of hand and foot to touch random balls for the balance training or test. Training results are calculated in two parts: fixed time – count score or fixed score – count the time. Figure 4 shows the limbs training interactive multimedia implementation. System components include Kinect, Notebook, TV Screen, etc. Subjects should be completed at the beginning of limbs skeleton position calibration and then follow-up training physical action. In the design of physical action divided into upper extremity (hand), lower extremity (foot) and the extremities (hands, feet, mix), etc., the subjects should follow the instructions on the screen to perform limb movements. In the process of body movements, the upper or lower extremity of a subject
W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Fig. 4. Interactive multimedia training. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/ THC-130769)
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Fig. 5. The scope of the stretching movements. (Colours are visible in the online version of the article; http://dx.doi.org/ 10.3233/THC-130769)
hits the ball, and then the device calculates the reaction time and velocity with the system time of a computer. The ball is randomly located in 3-dimensional space during the training activities period. Then, the upper or lower extremity of a subject hits the positioned of a ball and the proposed system automatically records the score. Following the completion of a series of training activities, the statistics and analysis, and the related information can be provided to medical personnel for reference and applications, as arrangements for training programmes and activities related to the reference. The degree of difficulty of the stretching movements is defined by the participant’s skeleton. To take into account safety during the activities, Fig. 5 demonstrates the scope of the stretching movements, where the 50%, 75% and 100% degree between Shoulder Centre and Wrist are easy, normal and difficult stretching, respectively, corresponding to the random balls appearing at specific spots designed by the software programmes. Figure 6 shows the coordinates of the target ball and hitting hand, with p0 (k) as the position of the k-th appearance ball and p(k) as the position of the k-th hitting hand. Derivation of the formula Eq. (5) shows the average velocity of the hitting hand. p0 (k) = (x0 (k), y0 (k), z0 (k)) p(k) = (x(k), y(k), z(k)) d(k) = (x(k) − x0 (k))2 + (y(k) − y0 (k))2 + (z(k) − z0 (k))2 Δt(k) = T0 (p0 (k)) − T (p(k))
(1) (2) (3) (4)
n
vave =
1 d(k) n Δt(k)
(5)
k=1
In people’s daily activities of static or dynamic performance, balance control plays a very important factor [14,15], where static equilibrium position is standing in a certain position with the ability to
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
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Fig. 6. Definition of coordinate system. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/ THC-130769)
maintain body balance, and dynamic balance refers to movement to another location while maintaining the body’s ability to balance. The TUG (Timed Up and Go) assesses an individual’s functional mobility [16]. This test records the time (in sec) required for a subject to stand up from a chair (seat height about 46 cm, handrail height about 65 cm), walk 10 feet forward, turn around, return to the chair, turn around and sit down. Timing of the test begins at the word “go” and stops when the subject sits back down on the chair. The longer the time needed, the worse the performance. The cutoff point of the use time is 13.5 seconds and has a good predictive ability for falls [17,18]. Its intratester and intertester reliability have been reported as high (ICC = 0.92–0.99) [16,19,20] and its test-retest reliability has been found to be acceptable (ICC = 0.56) in a group of community dwelling older adults [20]. The BBS (Berg Balance Scale) assigns an integer score of between 0 and 4 to the performance of each of 14 different tasks: 0 points means that it could not be done, 4 points indicates instructions completed; total score is 56 points, with higher scores indicating better balance ability. These tasks assess older adults’ balance performance in everyday activities (e.g. eyes closed, feet together, picking up an object, turning, alternate stepping, and narrowed base of support) [21]. Previous studies have shown the interrater reliability (ICC = 0.89), the group reliability (ICC = 0.90). In Taiwan, Berg Balance scale detection of the elderly in the community also has good reliability and validity of the performance group; inter-rater reliability was 0.87, the group reliability 0.77 [10]. Participants signed the institutional review board-approved informed consent form prior to being approached by TMU (Taipei Medical University Hospital) for this study. Each subject was first interviewed to obtain background information, exercise habits, medical history. The experiment was divided into two groups (experimental and control groups, each with four participants); the experimental group received six weeks’ (five sessions a week for a period of training principles, 30 minutes/each) intervention training, while the control group did not perform six weeks of training modules. The experimental group members used the Kinect device for training and testing; the system automatically recorded the skeleton coordinates and the use of time (in testing phase) to analyse movement velocity, responsiveness, etc. This was made available to clinical staff for reference. During training exercises, this device measured oxygen saturation (SpO2 ) and pulse rate (bpm) from the forefinger, and the real-time captured information was transmitted to the server computer via a wireless
W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention Table 2 Subject’s (control group) basic information
Table 1 Subject’s (experimental group) basic information Subject
Mr. A
Ms. B
Mr. C
Ms. D
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Subject
Ms. E
Mr. F
Mr. G
Ms. H
Sex ♂ ♀ ♂ ♀ Age 54 62 58 67 Height (cm) 176 157 181 152 Weight (kg) 73 53 78 51 Dominant side Left hand Left hand Right hand Right hand
Sex ♀ ♂ ♂ ♀ Age 56 70 68 75 Height (cm) 163 153 175 156 Weight (kg) 73 55 80 66 Dominant side Left hand Left hand Right hand Right hand
(a) Reaction time of the hand
(b) Reaction time of the foot
(c) Real-time SpO2 measured values (measured period: 10sec)
(d) Real-time pulse rate measured values (measured period: 10sec)
Fig. 7. Ms. B the three mode of average velocity performance (experimental group). (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/ THC-130769)
network. For participation in left-handedness using the system determines the same assessment results, to ensure the system’s stability and reproducibility level [14,15]. Experimental and control groups underwent the two performance-based evaluations (BBS and TUG), which were effectiveness evaluation. 3. Results The experimental project consists of two experimental groups over 50 years old, who can walk independently to conduct the health system testing. Tables 1 and 2 show the subjects’ basic information.
Week 6 54.19 ± 1.87 45.86 ± 2.68 21.20 ± 1.15 24.30 ± 1.12 1.11 ± 0.14 1.57 ± 0.34 1.30 ± 0.29 1.25 ± 0.12
Week 3 52.85 ± 1.92 43.95 ± 2.38 20.11 ± 1.20 22.80 ± 1.21 1.11 ± 0.21 1.58 ± 0.81 1.33 ± 0.64 1.28 ± 0.11
Dominant side (right hand) Week 0 Week 3 Velocity (Mr. C and MS. D) Left hand (cm/sec) 43.65 ± 2.18 45.95 ± 2.15 Right hand (cm/sec) 51.8 ± 1.68 53.68 ± 1.88 Left foot (cm/sec) 21.32 ± 1.28 23.92 ± 1.35 Right foot (cm/sec) 19.25 ± 1.31 20.81 ± 1.50 Reaction time (Mr. C and Ms. D) Left hand (sec) 1.72 ± 0.95 1.65 ± 0.92 Right hand (sec) 1.25 ± 0.15 1.05 ± 0.21 Left foot (sec) 1.38 ± 0.32 1.30 ± 0.23 Left foot (sec) 1.38 ± 0.62 1.33 ± 0.64
94–96 94–95 93–94 95–98 68–82 68–76 71–85 69–85
65–85 67–78 72–88 70–89
Week 2 (medium test)
94–95 93–95 92–94 94–96
Week 0 (pre-test)
67–81 69–77 72–87 70–81
94–96 93–95 94–96 95–97
Week 6 (post-test)
Table 5 Results of the evaluation scale Group Experimental Control Week 0 Week 3 Week 6 Week 0 Week 3 Week 6 BBS(score) 52.25 ± 2.22 53.50 ± 1.73 54.75 ± 0.5 50.50 ± 1.73 51.25 ± 0.96 51.75 ± 1.26 TUG(sec) 8.60 ± 0.54 8.00 ± 0.60 7.78 ± 0.48 9.80 ± 0.16 9.30 ± 0.18 8.90 ± 0.18 Notes: Values are expressed as mean ± SD.
SpO2 (%) Mr. A Mr. B Mr. C Mr. D Pulse rate (Beats/min) Mr. A Mr. B Mr. C Mr. D
Table 4 SpO2 and pulse rate measurement information (experimental group)
Dominant side (left hand) Week 0 Velocity (Mr. A and MS. B) Left hand (cm/sec) 50.88 ± 1.68 Right hand (cm/sec) 42.73 ± 2.32 Left foot (cm/sec) 19.13 ± 1.21 Right foot (cm/sec) 20.12 ± 1.18 Reaction time (Mr. A and Ms. B) Left hand (sec) 1.13 ± 0.15 Right hand (sec) 1.61 ± 0.72 Left foot (sec) 1.37 ± 0.52 Right foot (sec) 1.36 ± 0.18 Notes: Values are expressed as mean ± SD.
Table 3 Velocity and reaction time (experimental group)
1.58 ± 0.56 1.01 ± 0.14 1.28 ± 0.21 1.31 ± 0.66
46.36 ± 1.98 55.82 ± 1.95 25.18 ± 1.25 21.80 ± 1.25
Week 6
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Group Average value P (T t)
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Table 6 Results of the t-test BBS TUG Experimental Control Experimental Control 54.75 52.75 7.775 8.9 0.0069 0.0058
Figure 7 shows that the member Ms. B of the experimental group participates in virtual reality balance training with Kinect. The process of an exercise (Normal mode) measure a real-time information including SpO2, pulse rate, velocity and reaction time. The straight bar graph gives its performance of four extremities. Among them, Ms. B’s dominant left hand was better than right hand, her right foot was better than left foot. Real-time monitoring of SpO2 and pulse Rate during training process is presented very stable, which SpO2 range about 95%–97% and pulse rate range about 68–81 Beats/min. Table 3 shows that subjects were randomly assigned to either the experimental or control groups. The experimental group attended balance training (five sessions a week for six weeks) using an established protocol. The data from the balance training programme were assessed in the experimental group at the end of week 0, week 3 and week 6. The velocity and reaction time of Table 3 show that Mr. A and Ms. B are left-handed; the data reveal that the performance of the left-hand is better than the right-hand and the performance of the right-foot better than the left-foot. Also, Mr. C and Ms. D are right-handed; the data show that the performance of the right-hand is better than the left-hand and the performance of the left-foot better than the right-foot. Table 4 shows that real-time monitoring of SpO2 and pulse rate of the experimental group during the training process is very stable. The data of Table 4 show no statistically significant changes. Each member of the experimental and control groups at the six-week point measures three times (week 0 (pre-test), week 3(midterm-test) and week 6 (post-test)) scale assessments. Table 5 shows the results of evaluation scales (BBS and TUG) have significant progress. Table 6 shows the results of the t-test values; BBS test at week six was 0.0069. Also, the TUG test was 0.0058. Balance parameters were significantly improved in the experimental group (P < 0.01). This effect was also associated with a significant reduction in falls and lower levels of fear of falling (P < 0.01). 4. Discussion and conclusion According to the findings, a virtual reality scenario with interactive information platform successfully improves balance ability in a population of healthy community-dwelling elderly people. The balance training activities design a variety of indicators to help the elderly and to measure real-time bio-signals including SpO2 and pulse rate. The experimental group were also evaluated using the BBS and TUG during the six-week training activities. The results show that effective balance really improved. The experimental group were examined using balance assessment scale testing (TUG and BBS), and t-test determined that the means of two groups were statistically different from each other. Another outcome of the participating subjects’ performance and capabilities developed for the promotion of the balanced information platform is in line with demand, and the introduction of the practice with fun activities also helps balance the promotion for the prevention of falls. This study advanced balance ability and reduced the risk of falling among the elderly. The results show that the proposed method is effective. In future, we aim to research elderly people with a history of falls.
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In future study, we intend to attract potential participants to measure more effectively quantitative information for the assessment of further research. Excellent training activities improve the quality of exercises offered to healthy elderly adults at risk of falling and allow more timely intervention to prevent falls. Acknowledgements The authors wish to thank the National Science Council (NSC) in Taiwan (Grant Number: NSC 1022627-E-002-005-, NSC 102-2221-E-152-001-) for support this research. References [1] [2] [3]
Bureau of Health Promotion, Department of Health, Taiwan. Available from: http://www. bhp.doh.gov.tw. Robbin AS, Rubenstein LZ. Predictors of fall among elderly people. Arch lnt Med. 1989; 49, p.1628-1633. Daubney ME, Culham EG. Lower-extremity muscle fore and balance performance in adults aged 65 years and older. Physical Therapy. 1999; 79(12), p.1177-1185. [4] Robertson MC, Campbell AJ, Gardner MM, Devlin N. Preventing injuries in older people by preventing falls: a metaanalysis of individual-level data. J Am eriatr Soc. 2002; 50, p.905-911. [5] Taylor D, Stretton C. The otago exercise program, an evidencebased approach to falls prevention for older adults living in the community. Journal of Primary Health Care. 2004; 31(6). [6] Belgen B, Beninato M, Sullivan PE, Narielwalla K. The association of balance capacity and falls self-efficacy with history of falling in community-dwelling people with chronic stroke. The American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation. 2006; 87, p.554-561. [7] Ledin T, Kronhed AC, Moller C, Moller MOdkvist LM, Olsson. Effects of balance training in elderly evaluated by clinical tests and dynamic petrography. Journal of Vestibular Research. 1990; 1(2): p.129-138. [8] Ki Hun Cho, Kyoung Jin Lee, Chang Ho Song. Virtual-Reality Balance Training with a Video-Game System Improves Dynamic Balance in Chronic Stroke Patients. Tohoku J. Exp. Med., 2012; 228: p.69-74. [9] Gustavo Duque, Derek Boersma, Griselda Loza-Diaz, Sanobar Hassan, Hamlet Suarez, Dario Geisinger, Pushpa Suriyaarachchi, Anita Sharma, Oddom Demontiero. Effects of balance training using a virtual-reality system in older fallers. Clinical Interventions in Aging, 2013;8 p.257-263. [10] Wang CY, Hsieh CL, Olson SL, Wang CH, Sheu CF, Liang CC. Psychometric properties of the berg balance scale in a community-dwelling elderly resident population in Taiwan. Journal of Formosan Medical Association. 2006; 105(12): p.992-1000. [11] Tinetti ME, Williams TF, Mayewski R. Fall risk index for elderly patients based on number of chronic disabilities. Am J Med. 1986; 80: p.429-434. [12] Shumway Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community dwelling older adults using the time up & go test. Physical Therapy. 2000; 80(9): p.896-903. [13] Dite W, Temple VA. A clinical test of stepping and change of direction to identify multiple falling older adults. Archives of Physical Medicine & Rehabilitation. 2002; 83(11): p.1566-1571. [14] Gallahue DL, Ozmun JC. Understanding motor development: Infants, children, adolescents, adults. 5th ed. Singapore: McGraw-Hill; 2002. [15] Massion J, Woollacott MH. Posture and equilibrium.Clinical disorders of balance, posture and gait 1-18. New York: Oxford University; 1996. [16] Podsiadlo D, Richardson S. The timed ‘Up and Go’: a test of basic functional mobility for frail elderly persons. J Am Geri Soc. 1991; 39: 142-7. [17] American Association on Mental Retardation. Mental Retardation: Definition, Classification and Systems of Supports. 10th ed. 2007. [18] Albert M, Cook M, Hussey S. Assistive Technologies. 2nd ed. Principles and Practice; 2007. [19] Hughes C, Osman C, Woods AK. Relationship among performance on stair ambulation, Functional Reach, and Timed Up and Go tests in older adults. Issues on Ageing. 1998; 21: p.18-22. [20] Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther. 2000; 80: p.896–903. [21] Berg KO, Wood-Dauphinee SL, Williams JI, et al. Measuring balance in the elderly: validation of an instrument. Can J Public Health 1992; 83: p.7-11.
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Virtual reality system based on Kinect for the elderly in fall prevention W.-M. Hsieha,b , C.-C. Chenc,1 , S.-C. Wangd , S.-Y. Tana,1 , Y.-S. Hwanga , S.-C. Chene,h,1 , J.-S. Laif,1 and Y.-L. Chend,g,∗ a Department
of Electronic Engineering and Graduate Institute of Computer and Communication Engineering, National Taipei University of Technology, Taipei, Taiwan b Department of Electronic Engineering, Hwa Hsia Institute of Technology, Taipei, Taiwan c Department of Management Information System, Hwa Hsia Institute of Technology, Taipei, Taiwan d Department of Computer Science, National Taipei University of Education, Taipei, Taiwan e Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan f Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan g Department of Information Management, St. Mary’s Medicine, Nursing and Management College, Yilan, Taiwan h Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan Received 5 September 2013 Accepted 2 December 2013 Abstract. BACKGROUND: Daily life movements require balance ability. Good balance control is closely related to body stability and its development. Therefore, balance training is necessary for any age group. OBJECTIVE: This study proposes the combination of Kinect and virtual reality to build an information platform of interactive scenarios, for practice and evaluation of balance ability. Real-time monitoring of SpO2 , pulse rate, velocity and reaction time during the training process is presented for the training activities of elderly physical function. METHODS: Based on the indicators of balance ability, this information platform sets out various training activities to improve balance ability, making the supposedly tedious process fun and vivid and leading to much better training results. RESULTS: The data (SpO2 , pulse rate, velocity, reaction time) collected from this platform can be sorted and analysed, and the results used to evaluate the performance of balance training, and referenced for follow-up planning in the future. The real-time pulse rate and SpO2 measurement information indicating the training activities for the elderly to maintain physical function has a positive significance. A noninvasive and unconstrained real-time method to detect the pulse rate and SpO2 during exercises is presented. The results of balance assessment scale testing of BBS and TUG for the experimental group show that effective balance really improved. The difference between the experimental group and the control group was achieved by using paired t-test. The data were analysed by the descriptive statistics on significant level of P < 0.01. ∗
Corresponding author: Y.-L. Chen, Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, No. 134, Sec. 2, Heping E. Rd., Da-an District, Taipei City 106, Taiwan. Tel.: +886 2 2732 1104; Fax: +886 2 6639 6688; E-mail: [email protected]. 1 C.-C. Chen, S.-Y. Tan, S.-C. Chen and J.-S. Lai contributed equally to this study and should all be considered first authors. c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
CONCLUSIONS: From the training results of the participants, we know that the information platform developed for enhancing balance ability is in line with practical needs. Furthermore, the fun and interesting game-like exercises it introduces are very helpful in improving balance ability, and certainly in preventing falls. Keywords: Balance, fall, Kinect, virtual reality, information platform
1. Introduction Daily life movements such as standing, walking and jumping require balance ability. Good balance control is closely related to body stability and its development. According to medical research, people who become dizzy after taking drugs may have had their balance ability substantially affected, and are more likely to fall. This is especially the case as regards the elderly. Balancing means the ability to maintain stability in all kinds of movements and postures. Most human movements rely on the foundation of balance, and maintaining body balance is critical to human movements. Body movements are pulled by gravity; a movement can be performed only after the balancing point is achieved. Although normal people have good balance ability to deal with daily needs, dizziness caused by diseases or drugs – especially taking multiple drugs, such as sleeping pills and tranquilizers – can lead to falls. The Bureau of Health Promotion, Department of Health, points out that elderly people over the age of 65 are the group most at risk of dying from a fall. The statistics show that in 2008 there were about 125,000 patients hospitalised for fall injuries. All the evidence suggests that aging does have substantial impact on the ability of balance control [1]. This study proposes the combination of Kinect with virtual reality to build an information platform of interactive scenarios, for practices and evaluation on balance ability. Based on the indicators of balance ability, this platform sets out various training activities to improve balance ability, making the supposedly tedious process fun and vivid for much better training results. Furthermore, according to the literature, training with gaming patterns results in 30% reduction in falls. The data of this type of training, like time of use, scores and joint postures, can also be recorded and sent through the network to databases on remote computer servers. The data collected from this platform can be sorted and analysed, and the results then used to evaluate the performance of balance training, and referenced for follow-up planning in the future. In their studies on incidents of falling among older people, Robbin et al. [2] argue that abnormal gaits are to blame [2]. Daubney et al. [3] studied 50 elderly people over the age of 65, and analysed the relationship between their lower limb muscle strengths and balance. The results showed that those who fell without obvious reasons had less balance ability than those who never fell, and the lower limb muscle strength was closely related to balance ability. Therefore, the muscle strength measurement index provides a very high prediction performance [3]. The community research of Robertson et al. [4] showed that the elderly and other people who had suffered falls previously could do exercise training to reduce the incidents of falling and prevent fall injuries as well [4]. Taylor et al. [5] point out that certain programmes of exercise such as the Falls Management Exercise (FaME) and Otago Exercise (OEP) programmes are effective in returning falls patients to normal functional movement and can reduce the risk of falling by as much as 30% [5]. Belgen et al. [6] argue that individual body balance ability is related to the probability of fall incidence, and people who suffer more fall incidents have less balance abilities [6]. The research of Ledin et al. [7] also showed that the elderly undertaking a period of balance training could have their balance ability improved to prevent fall and bone fracture [7]. Cho et al. [8] highlight
W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Application
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Application (Game, Browser...etc)
Unity 3D Kinect
Unity 3D Development Interface
NB (WinOS)
KinectSDK (for Windows)
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Kinect SDK for Windows skeleton display
skeleton display
Fig. 1. Overall schematic diagram. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/THC-130769)
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Fig. 2. System structure diagram. (Colours are visible in the online version of the article; http://dx.doi.org/ 10.3233/THC-130769)
the effects of virtual reality balance training (VRBT) with a balance board game system on balance of chronic stroke patients. It leads to significant improvement in dynamic balance in chronic stroke patients with VRBT. VRBT is feasible and suitable for chronic stroke patients with balance deficit in clinical settings [8]. Duque et al. [8] researched a new virtual-reality system (the Balance Rehabilitation Unit [BRU]) as regards balance, falls and fear of falling in a population of community-dwelling older subjects with a known history of falls. BRU training is an effective and well-accepted intervention to improve balance, increase confidence and prevent falls in the elderly [9]. Mainly due to falls in the elderly balance function decline, falls can seriously affect their health and quality of life, so falls risk assessment tool ensure an estimate of each falls prediction performance. Clinical staff assessed by static balance, posture control capacity assessment, assessment of dynamic balance ability of the elderly and other methods to determine the risk of falls. Elderly falls through a variety of interventions can be done as prevention; the way in which motion has been shown to have the effect of reducing risk of falls. Several standard falls risk assessment tools are the Berg Balance Scale (BBS), Timed Up and Go (TUG), Four Square Step Test (FSST), etc., which are various ways to detect balance and falls prevention. In related research, it has been pointed out that the BBS for assessing balance in community-dwelling older adults is good for determining reliability and validity [10]. TUG test has a good predictive ability of falls. [11,12] FSST is a dynamic standing balance ability evaluation method [13].
2. Materials and methods Figure 1 is an overall schematic illustration, where Unity3D and Kinect SDK software tools in combination with Kinect sensors are used to show the action of the human skeleton displayed in the NB side. Figure 2 shows the system structure of this study, where the development process includes the hardware connections, signals receiving and analyses, movement identifying, etc. The hardware used is Kinect (for Windows) developed by Microsoft. The software application is developed with the Microsoft
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Fig. 3. The human body on a 20 frame locating point measured by the Kinect. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/THC-130769)
officially released Kinect SDK software tool along with Unity3D, a 3D-enabled game development software tool. The Kinect SDK tool includes all the required documents and API libraries and header files, while Unity3D is a comprehensive, 3D-game and 3D interactive tool for creativity development. Kinect is used to detect the human skeleton, and the detected 3D in-depth images are converted to the skeleton tracing system, which is able to trace up to 20 skeleton joints of the human body. The coordinates of the detected skeleton joints are then reflected in the 3D scenes developed with Kinect SDK programmes using Unity 3D’s libraries to visualise the body movements and present the visual reality simulation. Figure 3 shows how the Kinect tracks the human body on a 20 frame locating point; the captured coordinates (x, y , z ) are displayed on the screen, and the coordinate values can be used as a basis for object manipulation. The interactive multimedia system includes the Kinect, unity 3D and the proposed programme, which captures the upper or lower extremity of a subject to understand postures of the body. This study mainly focuses on fall prevention, and the proposed training and test items are designed by making references to the Four Square Step Test (FSST) method often used for examining balance ability; one of the designed activities uses the hand or foot or the combination of hand and foot to touch random balls for the balance training or test. Training results are calculated in two parts: fixed time – count score or fixed score – count the time. Figure 4 shows the limbs training interactive multimedia implementation. System components include Kinect, Notebook, TV Screen, etc. Subjects should be completed at the beginning of limbs skeleton position calibration and then follow-up training physical action. In the design of physical action divided into upper extremity (hand), lower extremity (foot) and the extremities (hands, feet, mix), etc., the subjects should follow the instructions on the screen to perform limb movements. In the process of body movements, the upper or lower extremity of a subject
W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Fig. 4. Interactive multimedia training. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/ THC-130769)
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Fig. 5. The scope of the stretching movements. (Colours are visible in the online version of the article; http://dx.doi.org/ 10.3233/THC-130769)
hits the ball, and then the device calculates the reaction time and velocity with the system time of a computer. The ball is randomly located in 3-dimensional space during the training activities period. Then, the upper or lower extremity of a subject hits the positioned of a ball and the proposed system automatically records the score. Following the completion of a series of training activities, the statistics and analysis, and the related information can be provided to medical personnel for reference and applications, as arrangements for training programmes and activities related to the reference. The degree of difficulty of the stretching movements is defined by the participant’s skeleton. To take into account safety during the activities, Fig. 5 demonstrates the scope of the stretching movements, where the 50%, 75% and 100% degree between Shoulder Centre and Wrist are easy, normal and difficult stretching, respectively, corresponding to the random balls appearing at specific spots designed by the software programmes. Figure 6 shows the coordinates of the target ball and hitting hand, with p0 (k) as the position of the k-th appearance ball and p(k) as the position of the k-th hitting hand. Derivation of the formula Eq. (5) shows the average velocity of the hitting hand. p0 (k) = (x0 (k), y0 (k), z0 (k)) p(k) = (x(k), y(k), z(k)) d(k) = (x(k) − x0 (k))2 + (y(k) − y0 (k))2 + (z(k) − z0 (k))2 Δt(k) = T0 (p0 (k)) − T (p(k))
(1) (2) (3) (4)
n
vave =
1 d(k) n Δt(k)
(5)
k=1
In people’s daily activities of static or dynamic performance, balance control plays a very important factor [14,15], where static equilibrium position is standing in a certain position with the ability to
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
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Fig. 6. Definition of coordinate system. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/ THC-130769)
maintain body balance, and dynamic balance refers to movement to another location while maintaining the body’s ability to balance. The TUG (Timed Up and Go) assesses an individual’s functional mobility [16]. This test records the time (in sec) required for a subject to stand up from a chair (seat height about 46 cm, handrail height about 65 cm), walk 10 feet forward, turn around, return to the chair, turn around and sit down. Timing of the test begins at the word “go” and stops when the subject sits back down on the chair. The longer the time needed, the worse the performance. The cutoff point of the use time is 13.5 seconds and has a good predictive ability for falls [17,18]. Its intratester and intertester reliability have been reported as high (ICC = 0.92–0.99) [16,19,20] and its test-retest reliability has been found to be acceptable (ICC = 0.56) in a group of community dwelling older adults [20]. The BBS (Berg Balance Scale) assigns an integer score of between 0 and 4 to the performance of each of 14 different tasks: 0 points means that it could not be done, 4 points indicates instructions completed; total score is 56 points, with higher scores indicating better balance ability. These tasks assess older adults’ balance performance in everyday activities (e.g. eyes closed, feet together, picking up an object, turning, alternate stepping, and narrowed base of support) [21]. Previous studies have shown the interrater reliability (ICC = 0.89), the group reliability (ICC = 0.90). In Taiwan, Berg Balance scale detection of the elderly in the community also has good reliability and validity of the performance group; inter-rater reliability was 0.87, the group reliability 0.77 [10]. Participants signed the institutional review board-approved informed consent form prior to being approached by TMU (Taipei Medical University Hospital) for this study. Each subject was first interviewed to obtain background information, exercise habits, medical history. The experiment was divided into two groups (experimental and control groups, each with four participants); the experimental group received six weeks’ (five sessions a week for a period of training principles, 30 minutes/each) intervention training, while the control group did not perform six weeks of training modules. The experimental group members used the Kinect device for training and testing; the system automatically recorded the skeleton coordinates and the use of time (in testing phase) to analyse movement velocity, responsiveness, etc. This was made available to clinical staff for reference. During training exercises, this device measured oxygen saturation (SpO2 ) and pulse rate (bpm) from the forefinger, and the real-time captured information was transmitted to the server computer via a wireless
W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention Table 2 Subject’s (control group) basic information
Table 1 Subject’s (experimental group) basic information Subject
Mr. A
Ms. B
Mr. C
Ms. D
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Subject
Ms. E
Mr. F
Mr. G
Ms. H
Sex ♂ ♀ ♂ ♀ Age 54 62 58 67 Height (cm) 176 157 181 152 Weight (kg) 73 53 78 51 Dominant side Left hand Left hand Right hand Right hand
Sex ♀ ♂ ♂ ♀ Age 56 70 68 75 Height (cm) 163 153 175 156 Weight (kg) 73 55 80 66 Dominant side Left hand Left hand Right hand Right hand
(a) Reaction time of the hand
(b) Reaction time of the foot
(c) Real-time SpO2 measured values (measured period: 10sec)
(d) Real-time pulse rate measured values (measured period: 10sec)
Fig. 7. Ms. B the three mode of average velocity performance (experimental group). (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/ THC-130769)
network. For participation in left-handedness using the system determines the same assessment results, to ensure the system’s stability and reproducibility level [14,15]. Experimental and control groups underwent the two performance-based evaluations (BBS and TUG), which were effectiveness evaluation. 3. Results The experimental project consists of two experimental groups over 50 years old, who can walk independently to conduct the health system testing. Tables 1 and 2 show the subjects’ basic information.
Week 6 54.19 ± 1.87 45.86 ± 2.68 21.20 ± 1.15 24.30 ± 1.12 1.11 ± 0.14 1.57 ± 0.34 1.30 ± 0.29 1.25 ± 0.12
Week 3 52.85 ± 1.92 43.95 ± 2.38 20.11 ± 1.20 22.80 ± 1.21 1.11 ± 0.21 1.58 ± 0.81 1.33 ± 0.64 1.28 ± 0.11
Dominant side (right hand) Week 0 Week 3 Velocity (Mr. C and MS. D) Left hand (cm/sec) 43.65 ± 2.18 45.95 ± 2.15 Right hand (cm/sec) 51.8 ± 1.68 53.68 ± 1.88 Left foot (cm/sec) 21.32 ± 1.28 23.92 ± 1.35 Right foot (cm/sec) 19.25 ± 1.31 20.81 ± 1.50 Reaction time (Mr. C and Ms. D) Left hand (sec) 1.72 ± 0.95 1.65 ± 0.92 Right hand (sec) 1.25 ± 0.15 1.05 ± 0.21 Left foot (sec) 1.38 ± 0.32 1.30 ± 0.23 Left foot (sec) 1.38 ± 0.62 1.33 ± 0.64
94–96 94–95 93–94 95–98 68–82 68–76 71–85 69–85
65–85 67–78 72–88 70–89
Week 2 (medium test)
94–95 93–95 92–94 94–96
Week 0 (pre-test)
67–81 69–77 72–87 70–81
94–96 93–95 94–96 95–97
Week 6 (post-test)
Table 5 Results of the evaluation scale Group Experimental Control Week 0 Week 3 Week 6 Week 0 Week 3 Week 6 BBS(score) 52.25 ± 2.22 53.50 ± 1.73 54.75 ± 0.5 50.50 ± 1.73 51.25 ± 0.96 51.75 ± 1.26 TUG(sec) 8.60 ± 0.54 8.00 ± 0.60 7.78 ± 0.48 9.80 ± 0.16 9.30 ± 0.18 8.90 ± 0.18 Notes: Values are expressed as mean ± SD.
SpO2 (%) Mr. A Mr. B Mr. C Mr. D Pulse rate (Beats/min) Mr. A Mr. B Mr. C Mr. D
Table 4 SpO2 and pulse rate measurement information (experimental group)
Dominant side (left hand) Week 0 Velocity (Mr. A and MS. B) Left hand (cm/sec) 50.88 ± 1.68 Right hand (cm/sec) 42.73 ± 2.32 Left foot (cm/sec) 19.13 ± 1.21 Right foot (cm/sec) 20.12 ± 1.18 Reaction time (Mr. A and Ms. B) Left hand (sec) 1.13 ± 0.15 Right hand (sec) 1.61 ± 0.72 Left foot (sec) 1.37 ± 0.52 Right foot (sec) 1.36 ± 0.18 Notes: Values are expressed as mean ± SD.
Table 3 Velocity and reaction time (experimental group)
1.58 ± 0.56 1.01 ± 0.14 1.28 ± 0.21 1.31 ± 0.66
46.36 ± 1.98 55.82 ± 1.95 25.18 ± 1.25 21.80 ± 1.25
Week 6
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
Group Average value P (T t)
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Table 6 Results of the t-test BBS TUG Experimental Control Experimental Control 54.75 52.75 7.775 8.9 0.0069 0.0058
Figure 7 shows that the member Ms. B of the experimental group participates in virtual reality balance training with Kinect. The process of an exercise (Normal mode) measure a real-time information including SpO2, pulse rate, velocity and reaction time. The straight bar graph gives its performance of four extremities. Among them, Ms. B’s dominant left hand was better than right hand, her right foot was better than left foot. Real-time monitoring of SpO2 and pulse Rate during training process is presented very stable, which SpO2 range about 95%–97% and pulse rate range about 68–81 Beats/min. Table 3 shows that subjects were randomly assigned to either the experimental or control groups. The experimental group attended balance training (five sessions a week for six weeks) using an established protocol. The data from the balance training programme were assessed in the experimental group at the end of week 0, week 3 and week 6. The velocity and reaction time of Table 3 show that Mr. A and Ms. B are left-handed; the data reveal that the performance of the left-hand is better than the right-hand and the performance of the right-foot better than the left-foot. Also, Mr. C and Ms. D are right-handed; the data show that the performance of the right-hand is better than the left-hand and the performance of the left-foot better than the right-foot. Table 4 shows that real-time monitoring of SpO2 and pulse rate of the experimental group during the training process is very stable. The data of Table 4 show no statistically significant changes. Each member of the experimental and control groups at the six-week point measures three times (week 0 (pre-test), week 3(midterm-test) and week 6 (post-test)) scale assessments. Table 5 shows the results of evaluation scales (BBS and TUG) have significant progress. Table 6 shows the results of the t-test values; BBS test at week six was 0.0069. Also, the TUG test was 0.0058. Balance parameters were significantly improved in the experimental group (P < 0.01). This effect was also associated with a significant reduction in falls and lower levels of fear of falling (P < 0.01). 4. Discussion and conclusion According to the findings, a virtual reality scenario with interactive information platform successfully improves balance ability in a population of healthy community-dwelling elderly people. The balance training activities design a variety of indicators to help the elderly and to measure real-time bio-signals including SpO2 and pulse rate. The experimental group were also evaluated using the BBS and TUG during the six-week training activities. The results show that effective balance really improved. The experimental group were examined using balance assessment scale testing (TUG and BBS), and t-test determined that the means of two groups were statistically different from each other. Another outcome of the participating subjects’ performance and capabilities developed for the promotion of the balanced information platform is in line with demand, and the introduction of the practice with fun activities also helps balance the promotion for the prevention of falls. This study advanced balance ability and reduced the risk of falling among the elderly. The results show that the proposed method is effective. In future, we aim to research elderly people with a history of falls.
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W.-M. Hsieh et al. / Virtual reality system based on Kinect for the elderly in fall prevention
In future study, we intend to attract potential participants to measure more effectively quantitative information for the assessment of further research. Excellent training activities improve the quality of exercises offered to healthy elderly adults at risk of falling and allow more timely intervention to prevent falls. Acknowledgements The authors wish to thank the National Science Council (NSC) in Taiwan (Grant Number: NSC 1022627-E-002-005-, NSC 102-2221-E-152-001-) for support this research. References [1] [2] [3]
Bureau of Health Promotion, Department of Health, Taiwan. Available from: http://www. bhp.doh.gov.tw. Robbin AS, Rubenstein LZ. Predictors of fall among elderly people. Arch lnt Med. 1989; 49, p.1628-1633. Daubney ME, Culham EG. Lower-extremity muscle fore and balance performance in adults aged 65 years and older. Physical Therapy. 1999; 79(12), p.1177-1185. [4] Robertson MC, Campbell AJ, Gardner MM, Devlin N. Preventing injuries in older people by preventing falls: a metaanalysis of individual-level data. J Am eriatr Soc. 2002; 50, p.905-911. [5] Taylor D, Stretton C. The otago exercise program, an evidencebased approach to falls prevention for older adults living in the community. Journal of Primary Health Care. 2004; 31(6). [6] Belgen B, Beninato M, Sullivan PE, Narielwalla K. The association of balance capacity and falls self-efficacy with history of falling in community-dwelling people with chronic stroke. The American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation. 2006; 87, p.554-561. [7] Ledin T, Kronhed AC, Moller C, Moller MOdkvist LM, Olsson. Effects of balance training in elderly evaluated by clinical tests and dynamic petrography. Journal of Vestibular Research. 1990; 1(2): p.129-138. [8] Ki Hun Cho, Kyoung Jin Lee, Chang Ho Song. Virtual-Reality Balance Training with a Video-Game System Improves Dynamic Balance in Chronic Stroke Patients. Tohoku J. Exp. Med., 2012; 228: p.69-74. [9] Gustavo Duque, Derek Boersma, Griselda Loza-Diaz, Sanobar Hassan, Hamlet Suarez, Dario Geisinger, Pushpa Suriyaarachchi, Anita Sharma, Oddom Demontiero. Effects of balance training using a virtual-reality system in older fallers. Clinical Interventions in Aging, 2013;8 p.257-263. [10] Wang CY, Hsieh CL, Olson SL, Wang CH, Sheu CF, Liang CC. Psychometric properties of the berg balance scale in a community-dwelling elderly resident population in Taiwan. Journal of Formosan Medical Association. 2006; 105(12): p.992-1000. [11] Tinetti ME, Williams TF, Mayewski R. Fall risk index for elderly patients based on number of chronic disabilities. Am J Med. 1986; 80: p.429-434. [12] Shumway Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community dwelling older adults using the time up & go test. Physical Therapy. 2000; 80(9): p.896-903. [13] Dite W, Temple VA. A clinical test of stepping and change of direction to identify multiple falling older adults. Archives of Physical Medicine & Rehabilitation. 2002; 83(11): p.1566-1571. [14] Gallahue DL, Ozmun JC. Understanding motor development: Infants, children, adolescents, adults. 5th ed. Singapore: McGraw-Hill; 2002. [15] Massion J, Woollacott MH. Posture and equilibrium.Clinical disorders of balance, posture and gait 1-18. New York: Oxford University; 1996. [16] Podsiadlo D, Richardson S. The timed ‘Up and Go’: a test of basic functional mobility for frail elderly persons. J Am Geri Soc. 1991; 39: 142-7. [17] American Association on Mental Retardation. Mental Retardation: Definition, Classification and Systems of Supports. 10th ed. 2007. [18] Albert M, Cook M, Hussey S. Assistive Technologies. 2nd ed. Principles and Practice; 2007. [19] Hughes C, Osman C, Woods AK. Relationship among performance on stair ambulation, Functional Reach, and Timed Up and Go tests in older adults. Issues on Ageing. 1998; 21: p.18-22. [20] Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther. 2000; 80: p.896–903. [21] Berg KO, Wood-Dauphinee SL, Williams JI, et al. Measuring balance in the elderly: validation of an instrument. Can J Public Health 1992; 83: p.7-11.
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