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Improving proprioceptive deficits after stroke through robot-assisted training of the upper limb: a pilot case report study R. Colombo, I. Sterpi, A. Mazzone, C. Delconte & F. Pisano To cite this article: R. Colombo, I. Sterpi, A. Mazzone, C. Delconte & F. Pisano (2016) Improving proprioceptive deficits after stroke through robot-assisted training of the upper limb: a pilot case report study, Neurocase, 22:2, 191-200, DOI: 10.1080/13554794.2015.1109667 To link to this article: http://dx.doi.org/10.1080/13554794.2015.1109667
Published online: 13 Nov 2015.
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Date: 16 March 2016, At: 07:37
NEUROCASE, 2016 VOL. 22, NO. 2, 191–200 http://dx.doi.org/10.1080/13554794.2015.1109667
Improving proprioceptive deficits after stroke through robot-assisted training of the upper limb: a pilot case report study R. Colomboa,b, I. Sterpia, A. Mazzoneb, C. Delcontec and F. Pisanoc
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a Service of Bioengineering, “Salvatore Maugeri” Foundation, IRCCS, Pavia, Italy; bService of Bioengineering, “Salvatore Maugeri” Foundation, IRCCS, Veruno, NO, Italy; cNeurologic Rehabilitation Division, “Salvatore Maugeri” Foundation, IRCCS, Veruno, NO, Italy
ABSTRACT
ARTICLE HISTORY
The purpose of this study was to determine whether a conventional robot-assisted therapy of the upper limb was able to improve proprioception and motor recovery of an individual after stroke who exhibited proprioceptive deficits. After robotic sensorimotor training, significant changes were observed in kinematic performance variables. Two quantitative parameters evaluating position sense improved after training. Range of motion during shoulder and wrist flexion improved, but only wrist flexion remained improved at 3-month follow-up. These preliminary results suggest that intensive robot-aided rehabilitation may play an important role in the recovery of sensory function. However, further studies are required to confirm these data.
Received 18 June 2015 Accepted 13 October 2015
1. Introduction Despite widespread prevention programs throughout the world and the advances in acute and subacute management and treatment protocols, stroke still remains one of the most common causes of adult disability, representing a serious global healthcare problem (Langhorne, Coupar, & Pollock, 2009). Most people surviving a stroke have to deal every day with important functional impairment of their upper and lower limbs and experience restrictions of their mobility, life activities, and quality of life (Broeks, Lankhorst, Rumping, & Prevo, 1999; Timmermans, Spooren, Kingma, & Seelen, 2010). Robot-assisted neurorehabilitation of the upper limb, thanks to its capacity to deliver highintensity training protocols, has the potential for a greater impact on impairment and motor function both in subacute and chronic stroke (Kwakkel, Kollen, & Krebs, 2008; Mehrholz, Hädrich, Platz, Kugler, & Pohl, 2012), and many different devices have been proposed both for clinical and home settings (Maciejasz, Eschweiler, Gerlach-Hahn, Jansen-Troy, & Leonhardt, 2014). Besides motor practice, proprioceptive feedback plays a critical role in the reorganization process and subsequent recovery of the neuromotor system as demonstrated by studies on usedependent plasticity (Goble, 2010; Goble & Anguera, 2010; Xerri, Merzenich, Peterson, & Jenkins, 1998). It plays a fundamental role in setting joint angles, maintaining posture and executing movement. For this reason, patients who exhibit proprioceptive deficits experience difficulties in controlling movements and maintaining their limbs in a steady posture without the assistance of vision (Wilson, Wong, & Gribble, 2010). In addition, remediation of sensory impairments is important to stroke survivors, but seems to be ignored in the rehabilitation process (Doyle, Bennett, & Dudgeon, 2014). A recent systematic review provided evidence that the literature on treating poststroke sensory impairments is sparse (Schabrun & Hillier, 2009).
Proprioception; sense of position; robot-assisted therapy; stroke; neurorehabilitation
Meta-analyses demonstrated that electrical stimulation has a moderate beneficial effect on sensory impairment and motor hand function (Celnik, Hummel, Harris-Love, Wolk, & Cohen, 2007; Conforto, Cohen, Dos Santos, Scaff, & Marie, 2007; Wu, Seo, & Cohen, 2006), but evidence for active sensory training, i.e., exercises in detecting, localizing and discriminating sensations, and proprioceptive training, is limited (Carey, Matyas, & Oke, 1993; Yekutiel & Guttman, 1993). The application of Butler’s neuromobilizations combined with proprioceptive neuromuscular facilitation (PNF) showed greater effectiveness in reducing sensory deficits (Chen & Shaw, 2014; Wolny, Saulicz, Gnat, & Kokosz, 2010). Some other studies have suggested that proprioception may be modulated by increasing its acuity during limb movements that are behaviorally relevant (Hospod, Aimonetti, Roll, & Ribot-Ciscar, 2007). In particular, Wong et al. showed that, following motor learning, proprioceptive acuity improved but only in the region of the workspace explored during learning (Wong, Wilson, & Gribble, 2011). However, there are few studies related to technology-aided rehabilitation focusing on the improvement of proprioception itself (Cho et al., 2014; De Santis et al., 2014; Lewek, Feasel, Wentz, Brooks, & Whitton, 2012). One example is the study by Cho et al. who used a virtual reality (VR) rehabilitation system to develop an interactive game that, by blocking visual feedback in specific phases of the motor task, was able to improve proprioceptive deficits of a group of stroke individuals (Cho et al., 2014). Recently, De Santis et al. proposed a method based on robotic training that is effective in enhancing kinesthetic acuity (De Santis et al., 2014). The aim of this study was to verify if it is possible to improve somatosensory function in stroke patients through conventional robot-assisted rehabilitation. Thus, as a
CONTACT R. Colombo [email protected] Present address of Irma Sterpi is Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milan, Italy. © 2015 Taylor & Francis
KEYWORDS
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preliminary study conducted to explore the possibility of implementing robot therapy for the rehabilitation of proprioception, in particular position sense, we present this case report on a stroke patient who exhibited proprioceptive deficits and underwent conventional robot training.
2. Materials and methods This study was carried out in accordance with the 13-item checklist of the CARE guidelines, to satisfy the need for precision, completeness, and transparency of case report studies (Gagnier et al., 2013).
he had not previously undergone a specific program to improve sensory function, the patient was hospitalized at our institute to continue physical and speech therapy in order to further improve his sensory, motor, and language symptoms. For this purpose, we decided to administer an extensive program of sensorimotor rehabilitation. His upper-extremity FuglMeyer (FM) score at admission was 20, suggesting severe disability in the execution of functional movements. Before our rehabilitation intervention and the evaluation tests, the patient signed informed consent in conformity with the Declaration of Helsinki of the World Medical Association.
2.2. Evaluation protocol
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2.1. Case description The patient was a 40-year-old man, with right-hand dominance, affected by ischemic stroke in the territory of the leftmiddle cerebral artery (Figure 1) which had occurred nine months before hospitalization in our rehabilitation institute. The patient was cooperative, alert, and oriented in time and space; for this reason, no specific test for evaluation of cognitive function was requested. He could autonomously gain standing position and change posture (e.g., sit-to-stand), but suffered from right hemiparesis that produced altered gait patterns during walking, reduced mobility of the upper right limb and severe reduced mobility of the hand (no grasp function). The patient had nonfluent aphasia diagnosed on the basis of a formalized language examination (Aachen Aphasia Test) evaluating verbal expression, understanding of spoken and written language, naming repetition, reading, and writing (Luzzatti & De Bleser, 1996). At the time of the robot-training intervention, his verbal comprehension was substantially preserved. In the subacute phase, he had received conventional physical therapy and speech rehabilitation in another rehabilitation hospital. Considering his young age and the fact that
On admission, the patient was assessed using a standard evaluation procedure that is currently applied to patients to check if they fulfill criteria for robot-assisted therapy. Specifically, admission to robot treatment is based on the presence, evaluated by an expert neurologist, of at least 10° of motion in the treated joints (shoulder and elbow) (Colombo et al., 2005). In addition, cognitive state is evaluated to ascertain that patients are able to fully understand and follow instructions. Neglect is excluded by means of the Albert’s test (Fullerton, McSherry, & Stout, 1986). The presence of severe elbow contractures, severe visual deficits or apraxia, and pain constitute exclusion criteria. Besides these criteria, the neurologist clinically investigates the presence of any somatosensory deficit of the hemiparetic hand and the preservation of movement function in the contralateral limb. The patient’s capacity of sensation was tested using parts (sense of touch and position) of the Rivermead Assessment of Somatosensory Performance (RASP). Proprioception was preliminarily tested only in the affected wrist and thumb through full range of motion (ROM) (Busse & Tyson, 2009; Winward, Halligan, & Wade, 2002). The patient, with eyes closed during passive flexion and extension of each joint, was asked in six trials to indicate when he felt the joint moving and the direction of movement (score = 0 both for position sense and movement direction). In addition, touch sensation of the palm and thumb of the affected hand were tested always with the patient’s eyes closed (score = 3 for palm and score = 2 for thumb). In accordance with RASP scoring classification, on the basis of the number of correct scores, the sensation was classified as absent, impaired, or normal. After this preliminary visit, our patient was enrolled in the robot-training program and underwent the following assessments as part of the study protocol: (a) evaluation before (t0), after training (t1), and at 3month follow-up (t2) of standardized clinical scales; and (b) quantitative evaluation of the sense of position and six robot measured parameters at the start and end of training. In addition, robot-measured parameters were evaluated during each training session.
2.3. Clinical evaluation tools
Figure 1. Computer tomography scan sample image of patient described in this case report. The patient suffered from ischemic stroke in the territory of the leftmiddle cerebral artery.
The level of impairment of the patient’s right arm was evaluated by the upper-limb section of the FM assessment scale (FM range = 0–66) (Fugl-Meyer, Jääskö, Leyman, Olsson, & Steglind, 1975). Further, the FM assessment included
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evaluation of sensation with light touch at the volar side of the forearm and palmar surface of the hand, and position sense of the glenohumeral, elbow, wrist, and thumb (interphalangeal) joints (Platz, Pinkowski, van Wijck, & Johnson, 2005). Muscle tone and spasticity was evaluated by the modified Ashworth scale (MAS) (Bohannon & Smith, 1987). Muscle strength was evaluated by the muscular manual test (MMT) performed in standardized positions (Schwartz, Cohen, Herbison, & Shah, 1992). Finally, active movement joint limitation was evaluated by measuring shoulder, elbow, and wrist active ROM using a standard goniometer. All clinical assessments were carried out by trained therapists not involved in the training protocol.
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autonomously maintain a grasp, and for this reason, his hand was fastened to the robot-handle by a special strap. The left handle, the “active handle,” was independent of the robot and could be actively moved in the workspace by the subject using the intact (left) arm. The active- and passive-hand raw positions (i.e., the Cartesian position of the center of the handles) were artificially mirrored across the x-coordinate in correspondence to the midsagittal axis. In this way, the actual and desired positions would overlap in the case of perfect matching. In accordance with Dukelow et al. (2010), the sense of position was evaluated by computing the following quantitative parameters.
2.4.1. Variability
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2.4. Quantitative evaluation of proprioception Quantitative evaluation of the sense of position was carried out through a specific device consisting of the 2-DoF robot device “Braccio di Ferro” (Iron Arm) (Casadio, Sanguineti, Morasso, & Arrichiello, 2006), the same employed for robot therapy (Figure 2a), combined with a low-cost motion analysis system composed of a standard digital camera (Sony®, DCRPC1000E). The patient was blindfolded with a black tissue mask so as to prevent use of visual feedback during evaluation. He was comfortably seated at the robot with his midsagittal axis aligned with the center of the robot-workspace and grasped two handles, during a limb position-matching task. These handles had some colored markers that allowed measurement of the position of both upper limbs in the horizontal plane by analyzing the images captured by the digital camera. Details about the device and procedure can be found elsewhere (Cusmano et al., 2014). In the present study, the right handle was the “passive handle,” i.e., the robot-handle grasped with the impaired hand-limb and passively moved through the workspace. Due to the severity of hand impairment, the patient was unable to
(A)
It is obtained by computing the standard deviation of the active-hand positions for each target in the x-coordinate (Varx; note that Var stands for variability and not for variance) and y-coordinate (Vary), and the variability for both coordinates combined (Varxy)
2.4.2. Spatial contraction/expansion That is, the range/area of the workspace matched by the active (left) hand relative to that of the passive (right) one. The values below 1 were obtained when the range of space explored by the active hand was smaller than that obtained by the passive hand, so indicating a spatial contraction. Conversely, spatial expansion was indicated by the values above 1. The procedure was performed to compute the parameters along the x (Cont/Expx), y (Cont/Expy), and both coordinates (Cont/Expxy).
2.4.3. Systematic shifts We computed the systematic shifts for each target for the x-coordinate (Shiftx), y-coordinate (Shifty), and both coordinates (Shiftxy).
(B)
Digital Camera
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Figure 2. Robot devices used for evaluation and training of the patient. (a) Device “Braccio di Ferro” during the evaluation of sense of position. (b) Wrist manipulator and (c) shoulder–elbow manipulator (“Braccio di Ferro”) during robot therapy.
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2.4.4. Mean error The mean value of the absolute differences between, respectively, the actual and the desired positions for the x (Errorx), y (Errory), and xy (Errorxy) coordinates. This parameter in reference to normative data should indicate the presence of proprioceptive deficits.
2.5. Quantitative evaluation during robot therapy At the start, end, and during robot-assisted treatment, consisting of a sequence of point-to-point reaching tasks with visual feedback (eyes open), we recorded the position of the robot end-effector at 100 Hz sampling rate. Then, we computed the following performance parameters (Colombo et al., 2008, 2014).
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2.5.1. Active movement index The active movement index (AMI) represents the patient’s ability to autonomously execute the requested motor task without any assistance of the robot. It is calculated as the percentage of trajectory travelled by means of the patient’s voluntary activity.
2.5.2. Mean velocity The mean value of the velocity of the robot end-effector measured during each reaching movement.
2.5.3. Movement accuracy It measures the mean absolute distance (MD) of each point of the actual path travelled by the subject from the theoretical path. When this parameter approximates zero, movement accuracy will be very high. Actually, it measures the error of accuracy; hence, a decrease in this index during training indicates an improvement of accuracy in the motor task execution.
2.5.4. Normalized path length The normalized path length (nPL) is obtained by computing the path length of the trajectory travelled by the patient to reach the target and normalized to the theoretical path. It estimates the error of movement efficiency; therefore, decreasing values during training reflect an improvement of efficiency in the motor task execution.
2.5.5. Movement smoothness This was estimated by two different parameters: (a) smoothness of the reaching movement (SM), computed as the ratio between the peak tangential speed and the mean speed of the robot end-effector (Bosecker, Dipietro, Volpe, & Krebs, 2010); and (b) spectral arc length (SAL), assessing the small oscillations in the trajectory speed profile that correspond to frequencies higher than the underlying movement (Balasubramanian, Melendez-Calderon, & Burdet, 2012). Both these parameters have been recently demonstrated to be reliable measures of movement smoothness (Colombo et al., 2014). The performance parameters were always measured during the voluntary activity (unassisted) phase of each reaching movement, and averaged so as to obtain for each parameter one mean value for each training session.
2.6. Training devices and rehabilitation intervention During his stay in our rehabilitation unit, the patient was treated using two robot devices: the 2-DoF elbow–shoulder manipulator “Braccio di Ferro” and 1-DoF wrist manipulator (Figure 2) (Casadio et al., 2006; Colombo et al., 2005). The end-effector of both devices consisted of a handle including a force sensor, which is grasped by the patient and moved through the respective workspace. The first one enabled the patient to execute reaching movements in the horizontal plane, the second allowed only training of flexion and extension of the wrist joint on the horizontal plane. The robot controller allowed execution both of completely voluntary movements and of “shared” controlled movements in which the device assisted the subject to complete the part of the task he was unable to do by means of voluntary effort. The assistance consisted of a constant force whose magnitude was set at the beginning of training in order to be able to passively drive the patient’s arm to the target in all the positions of the reachable workspace. It was gradually applied in a ramp-shape mode lasting 2 s, only when the patient was unable to autonomously complete the motor task. In practice, the patient started the task by trying to move the robot handle without any assistance. The robot controller continuously evaluated the current position of the handle and, if it remained for 3 s in the same place, then applied the assistance force so as to guide the patient’s arm to the target position (activity triggered assistance). During the training, depending on the robot device, the patient had to complete a motor task consisting of a sequence of pointto-point reaching movements in the shape of a geometrical figure (square) or a fully active or active-assisted wrist flexion and extension movement. Details of the administered tasks and procedures have been extensively reported before (Colombo et al., 2005, 2008). In this patient, a validated algorithm automatically changed the difficulty level of the task, by changing the square edge size from 150 mm to 220 mm at training session 9 based on his performance evaluated on the three robot-measured parameters AMI, mean velocity (MV), and nPL (Colombo, Sterpi, Mazzone, Delconte, & Pisano, 2012). The patient underwent the training twice a day, 5 days a week for 3½ weeks with the elbow– shoulder and wrist manipulators. Each training session consisted of four cycles of exercise lasting 5 min each followed by a 3 min resting period. On the same days as robot treatment, he underwent physical therapy including treatment for upper and lower limbs performed by professional therapists for 45 min/day, according to the Italian Stroke Prevention and Educational Awareness Diffusion (SPREAD) guidelines.
3. Outcomes The robot-assisted therapy was well accepted and tolerated by the patient who executed a total of 4864 reaching movements with the elbow–shoulder manipulator and 1484 extension– flexion movements with the wrist manipulator. On average, including resting periods and the time for connection/disconnection to the robots, our patient received 96 min/day of
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robot therapy for a total of 1536 min of treatment. No adverse events were observed during training.
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3.1. Clinical scale measures Table 1 reports the values of the clinical scales measured before and after training and at 3-month follow-up. The RASP assessment of sensation during the preliminary neurological visit showed that our patient had touch sensation absent and proprioception impaired. No clinical changes were observed at the end of training and follow-up in the FM scale, touch sensation, and position sense. The MAS was unchanged in the shoulder and wrist, and showed a 1-point reduction after training in the hand and elbow but which returned to the pretreatment value at follow-up. A slight improvement was observed in the MMT at the wrist joint and in elbow flexion. The active ROM of the shoulder was improved (15°) during flexion at the end of training, but returned to pretreatment values at follow-up. Only the wrist joint exhibited a slight improvement of wrist flexion (20°) that was maintained at follow-up.
Table 1. Clinical scale values measured before, after training and at 3-month follow-up. Clinical scale Fugl-Meyer (0–66) MAS
MMT (Flex./Ext.)
Active ROM (Flex./Ext.) Touch sensation Position sense
Joint/Site Upper limb Shoulder Elbow Wrist Hand Shoulder Elbow Wrist Hand/Fingers Shoulder Elbow Wrist Forearm Hand-palm Glenohumeral Elbow Wrist Thumb
Pre 20 0 1 1 2 2−/3− 3+/4 1/1 2−/0 45°/40° 140°/180° 0°/0° Dysesthesia Dysesthesia Normal Normal Absent Absent
Post 20 0 0 1 1 2−/3 4/4 2−/1 2−/0 60°/40° 140°/180° 20°/0° Dysesthesia Dysesthesia Normal Normal Absent Absent
PRE-Treatment
3-month 20 0 1 1 1 2−/3 4/4 2−/1 2−/0 45°/40° 140°/180° 20°/0° Dysesthesia Dysesthesia Normal Normal Absent Absent
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3.2. Changes in sense of position after treatment Figure 3 reports the patient’s sense of position evaluated before (a) and after (b) 3 weeks of training with the robotic devices; in addition, the performance pattern of a healthy subject is included for comparison. Specifically, the black squares and solid connecting lines represent the workspace positions through which the impaired (right) hand was passively moved by the robot. The subject’s performance is graphically summarized by reporting with black points, for each target location, the active hand’s mean position mirrored across the x-coordinate with respect to the midsagittal axis. In addition, the overlapped confidence ellipse represents one standard deviation around the mean target position (within-subject variability). The dashed line represents the equivalent geometric shape obtained during the mirror positioning task. It is evident that the performance obtained after training is clearly different (improved) from that obtained before training but, nevertheless, different from that of a healthy subject who in practice provided a nice overlap between active and passive limb patterns. This result is reflected by the changes observed in the quantitative parameters reported in Table 2. In fact, even if the patient did not show significant changes in variability (i.e., consistency of the active hand positioning), the Shiftxy (Pre = 11.83; Post = 10.21) and Errorxy (Pre = 11.92; Post = 10.36) decreased after robot treatment, the change being mainly evident along the y-axis. The Contr/Expy (Pre = 0.25; Post = 0.53) and Contr/Expxy (Pre = 0.06; Post = 0.13) values were double the initial values, but only the Contr/Expy had an increment higher than its minimal detectable change (Cusmano et al., 2014). In addition, the last two columns of Table 2 report the 5th and 95th percentiles computed in accordance with normative values reported by Herter, Scott, and Dukelow (2014). It is worth nothing that these normative data were collected using the same experimental protocol but with a different device, which provided the position of the index fingertips instead of the hand center (grasping center), as in our case. In spite of this, the abovereported parameters exhibit values that are outside the normative range but which, after sensorimotor training, move toward the normal confidence limits.
POST-Treatment
Healthy Subject
Figure 3. Changes in sense of position observed in our patient evaluated before (Pre) and after (Post) robot-assisted rehabilitation of the upper limb. Note that the pattern of points and dashed lines refers to mirror positioning performance of the unimpaired hand-arm and that the pattern improvement was obtained mainly in the vertical direction. However, both patterns are clearly different from that of a healthy subject.
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Table 2. Values obtained by the sense of position (SoP) parameters of our patient after stroke. SoP parameter Varx (cm) Vary (cm) Varxy (cm) Shiftx (cm) Shifty (cm) Shiftxy (cm) Cont/Expx Cont/Expy Cont/Expxy Errorx (cm) Errory (cm) Errorxy (cm)
Normality range Pre 2.40 1.83 3.05 −1.10 9.51 11.83 0.26 0.25 0.06 5.73 9.53 11.92
Post 2.81 2.10 3.53 0.14 7.82 10.21 0.25 0.53 0.13 5.76 7.90 10.36
Mean 2.92 1.33 3.27 0.48 −0.62 4.01 0.78 1.01 0.79 4.24 1.62 4.68
SD 0.76 0.33 0.78 3.75 1.19 1.80 0.17 0.07 0.19 1.66 0.57 1.58
5th Perc. 2.02 0.88 1.98 −6.50 −4.17 1.04 – – 0.53 – – 3.14
95th Perc. 4.61 2.00 4.29 7.24 3.61 8.19 – – 1.20 – – 8.28
3.3. Changes of motor performance after treatment For the sake of conciseness, we report here only the parameter values obtained with the shoulder and elbow manipulator. Visual inspection of the trajectories travelled by the patient at the start and end of training (Figure 4) shows that at the end of training, they were more stereotyped and much more similar to the theoretical pattern than at the beginning of training. In addition, the size of the shape (i.e., the edge of the square) was increased, signifying an increase in the difficulty level. Figure 5 reports two typical tangential speed profiles obtained during the execution of a single square task of the training sessions reported in Figure 4 (i.e., sessions 2 and 30). The number of peaks in the profile is directly related to movement smoothness. It is worth noting, for each square edge, the reduction of the number of peaks obtained at the end of training compared to the beginning, testifying an improvement of movement smoothness after training. In accordance with our previous work (Panarese, Colombo, Sterpi, Pisano, & Micera, 2012), different movement directions exhibit different
4. Discussion The original hypothesis of this single-case pilot study was that sensorimotor training provided by the robot devices could improve not only the motor performance of the impaired arm but also the impaired sensory performance thus allowing improvement of the patient’s proprioception and hence of sense of position. In other words, we sought a preliminary answer to the question if limb position sense can be trained in a patient after stroke, and if there is any evidence of improvements in spatial movement accuracy. This hypothesis was
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Except for Cont/Expxy, the mean values and standard deviations refer to six positionings for each of the nine targets of the evaluation task. The normality range mean and s.d. values reported for comparison are taken from a previously published study (Cusmano et al., 2014). The 5th and 95th percentile columns were computed according to normative values reported in the literature (Herter et al., 2014).
degrees of improvement in performance. Specifically, the left edge of the square involving shoulder extension exhibits the worst smoothness performance (more jerky movement) at the end of training. Data analysis for the assessment of motor performance improvement involved measuring the above parameters for each sequence (i.e., the square) of point-to-point reaching movements executed during the two daily training sessions of the rehabilitation program. Specifically, our patient could execute many reaching sequences during a training session: from a minimum of 24 to a maximum of 46 depending on the session number, difficulty level of the task, etc. Therefore, we were able to compare data obtained in the first three (pretreatment) and in the last three training sessions (post-treatment) using Student’s t-test for repeated measures. Table 3 reports the results of the PRE vs. POST comparison. All robot measured parameters significantly improved after training: MV, MD, nPL, and SAL were those that exhibited the most important changes. Figure 6 reports the time course of recovery of the AMI, MV, nPL, SM, and SAL parameters over 32 training sessions. AMI shows that, using visual feedback, from the beginning of training our patient could execute most of the requested motor tasks by voluntary activity (without need for assistance). In most of the other parameters, one can clearly see an exponential increasing/decreasing pattern of improvement typical of stroke recovery, in which the largest improvements are observed early after training onset and subsequently gradually taper off.
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Table 3. Mean values and standard deviations of the kinematic parameters at PRE and POST training and the results of the PRE vs. POST comparison. Kinematic parameter AMI (%) MV (mm/s) MD (mm) nPL (a.u.) SM (a.u.) SAL (a.u.)
PRE Mean 98.12 38.41 14.48 2.78 0.25 −3.97
POST SD 5.79 7.43 4.32 1.80 0.04 0.95
Mean 100.00 75.12 9.72 1.36 0.30 −2.22
t −2.81 −27.87 8.02 6.76 −8.32 −13.89
SD 0.00 7.91 2.59 0.15 0.03 0.27
p-Value
ISSN: 1355-4794 (Print) 1465-3656 (Online) Journal homepage: http://www.tandfonline.com/loi/nncs20
Improving proprioceptive deficits after stroke through robot-assisted training of the upper limb: a pilot case report study R. Colombo, I. Sterpi, A. Mazzone, C. Delconte & F. Pisano To cite this article: R. Colombo, I. Sterpi, A. Mazzone, C. Delconte & F. Pisano (2016) Improving proprioceptive deficits after stroke through robot-assisted training of the upper limb: a pilot case report study, Neurocase, 22:2, 191-200, DOI: 10.1080/13554794.2015.1109667 To link to this article: http://dx.doi.org/10.1080/13554794.2015.1109667
Published online: 13 Nov 2015.
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Date: 16 March 2016, At: 07:37
NEUROCASE, 2016 VOL. 22, NO. 2, 191–200 http://dx.doi.org/10.1080/13554794.2015.1109667
Improving proprioceptive deficits after stroke through robot-assisted training of the upper limb: a pilot case report study R. Colomboa,b, I. Sterpia, A. Mazzoneb, C. Delcontec and F. Pisanoc
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a Service of Bioengineering, “Salvatore Maugeri” Foundation, IRCCS, Pavia, Italy; bService of Bioengineering, “Salvatore Maugeri” Foundation, IRCCS, Veruno, NO, Italy; cNeurologic Rehabilitation Division, “Salvatore Maugeri” Foundation, IRCCS, Veruno, NO, Italy
ABSTRACT
ARTICLE HISTORY
The purpose of this study was to determine whether a conventional robot-assisted therapy of the upper limb was able to improve proprioception and motor recovery of an individual after stroke who exhibited proprioceptive deficits. After robotic sensorimotor training, significant changes were observed in kinematic performance variables. Two quantitative parameters evaluating position sense improved after training. Range of motion during shoulder and wrist flexion improved, but only wrist flexion remained improved at 3-month follow-up. These preliminary results suggest that intensive robot-aided rehabilitation may play an important role in the recovery of sensory function. However, further studies are required to confirm these data.
Received 18 June 2015 Accepted 13 October 2015
1. Introduction Despite widespread prevention programs throughout the world and the advances in acute and subacute management and treatment protocols, stroke still remains one of the most common causes of adult disability, representing a serious global healthcare problem (Langhorne, Coupar, & Pollock, 2009). Most people surviving a stroke have to deal every day with important functional impairment of their upper and lower limbs and experience restrictions of their mobility, life activities, and quality of life (Broeks, Lankhorst, Rumping, & Prevo, 1999; Timmermans, Spooren, Kingma, & Seelen, 2010). Robot-assisted neurorehabilitation of the upper limb, thanks to its capacity to deliver highintensity training protocols, has the potential for a greater impact on impairment and motor function both in subacute and chronic stroke (Kwakkel, Kollen, & Krebs, 2008; Mehrholz, Hädrich, Platz, Kugler, & Pohl, 2012), and many different devices have been proposed both for clinical and home settings (Maciejasz, Eschweiler, Gerlach-Hahn, Jansen-Troy, & Leonhardt, 2014). Besides motor practice, proprioceptive feedback plays a critical role in the reorganization process and subsequent recovery of the neuromotor system as demonstrated by studies on usedependent plasticity (Goble, 2010; Goble & Anguera, 2010; Xerri, Merzenich, Peterson, & Jenkins, 1998). It plays a fundamental role in setting joint angles, maintaining posture and executing movement. For this reason, patients who exhibit proprioceptive deficits experience difficulties in controlling movements and maintaining their limbs in a steady posture without the assistance of vision (Wilson, Wong, & Gribble, 2010). In addition, remediation of sensory impairments is important to stroke survivors, but seems to be ignored in the rehabilitation process (Doyle, Bennett, & Dudgeon, 2014). A recent systematic review provided evidence that the literature on treating poststroke sensory impairments is sparse (Schabrun & Hillier, 2009).
Proprioception; sense of position; robot-assisted therapy; stroke; neurorehabilitation
Meta-analyses demonstrated that electrical stimulation has a moderate beneficial effect on sensory impairment and motor hand function (Celnik, Hummel, Harris-Love, Wolk, & Cohen, 2007; Conforto, Cohen, Dos Santos, Scaff, & Marie, 2007; Wu, Seo, & Cohen, 2006), but evidence for active sensory training, i.e., exercises in detecting, localizing and discriminating sensations, and proprioceptive training, is limited (Carey, Matyas, & Oke, 1993; Yekutiel & Guttman, 1993). The application of Butler’s neuromobilizations combined with proprioceptive neuromuscular facilitation (PNF) showed greater effectiveness in reducing sensory deficits (Chen & Shaw, 2014; Wolny, Saulicz, Gnat, & Kokosz, 2010). Some other studies have suggested that proprioception may be modulated by increasing its acuity during limb movements that are behaviorally relevant (Hospod, Aimonetti, Roll, & Ribot-Ciscar, 2007). In particular, Wong et al. showed that, following motor learning, proprioceptive acuity improved but only in the region of the workspace explored during learning (Wong, Wilson, & Gribble, 2011). However, there are few studies related to technology-aided rehabilitation focusing on the improvement of proprioception itself (Cho et al., 2014; De Santis et al., 2014; Lewek, Feasel, Wentz, Brooks, & Whitton, 2012). One example is the study by Cho et al. who used a virtual reality (VR) rehabilitation system to develop an interactive game that, by blocking visual feedback in specific phases of the motor task, was able to improve proprioceptive deficits of a group of stroke individuals (Cho et al., 2014). Recently, De Santis et al. proposed a method based on robotic training that is effective in enhancing kinesthetic acuity (De Santis et al., 2014). The aim of this study was to verify if it is possible to improve somatosensory function in stroke patients through conventional robot-assisted rehabilitation. Thus, as a
CONTACT R. Colombo [email protected] Present address of Irma Sterpi is Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milan, Italy. © 2015 Taylor & Francis
KEYWORDS
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preliminary study conducted to explore the possibility of implementing robot therapy for the rehabilitation of proprioception, in particular position sense, we present this case report on a stroke patient who exhibited proprioceptive deficits and underwent conventional robot training.
2. Materials and methods This study was carried out in accordance with the 13-item checklist of the CARE guidelines, to satisfy the need for precision, completeness, and transparency of case report studies (Gagnier et al., 2013).
he had not previously undergone a specific program to improve sensory function, the patient was hospitalized at our institute to continue physical and speech therapy in order to further improve his sensory, motor, and language symptoms. For this purpose, we decided to administer an extensive program of sensorimotor rehabilitation. His upper-extremity FuglMeyer (FM) score at admission was 20, suggesting severe disability in the execution of functional movements. Before our rehabilitation intervention and the evaluation tests, the patient signed informed consent in conformity with the Declaration of Helsinki of the World Medical Association.
2.2. Evaluation protocol
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2.1. Case description The patient was a 40-year-old man, with right-hand dominance, affected by ischemic stroke in the territory of the leftmiddle cerebral artery (Figure 1) which had occurred nine months before hospitalization in our rehabilitation institute. The patient was cooperative, alert, and oriented in time and space; for this reason, no specific test for evaluation of cognitive function was requested. He could autonomously gain standing position and change posture (e.g., sit-to-stand), but suffered from right hemiparesis that produced altered gait patterns during walking, reduced mobility of the upper right limb and severe reduced mobility of the hand (no grasp function). The patient had nonfluent aphasia diagnosed on the basis of a formalized language examination (Aachen Aphasia Test) evaluating verbal expression, understanding of spoken and written language, naming repetition, reading, and writing (Luzzatti & De Bleser, 1996). At the time of the robot-training intervention, his verbal comprehension was substantially preserved. In the subacute phase, he had received conventional physical therapy and speech rehabilitation in another rehabilitation hospital. Considering his young age and the fact that
On admission, the patient was assessed using a standard evaluation procedure that is currently applied to patients to check if they fulfill criteria for robot-assisted therapy. Specifically, admission to robot treatment is based on the presence, evaluated by an expert neurologist, of at least 10° of motion in the treated joints (shoulder and elbow) (Colombo et al., 2005). In addition, cognitive state is evaluated to ascertain that patients are able to fully understand and follow instructions. Neglect is excluded by means of the Albert’s test (Fullerton, McSherry, & Stout, 1986). The presence of severe elbow contractures, severe visual deficits or apraxia, and pain constitute exclusion criteria. Besides these criteria, the neurologist clinically investigates the presence of any somatosensory deficit of the hemiparetic hand and the preservation of movement function in the contralateral limb. The patient’s capacity of sensation was tested using parts (sense of touch and position) of the Rivermead Assessment of Somatosensory Performance (RASP). Proprioception was preliminarily tested only in the affected wrist and thumb through full range of motion (ROM) (Busse & Tyson, 2009; Winward, Halligan, & Wade, 2002). The patient, with eyes closed during passive flexion and extension of each joint, was asked in six trials to indicate when he felt the joint moving and the direction of movement (score = 0 both for position sense and movement direction). In addition, touch sensation of the palm and thumb of the affected hand were tested always with the patient’s eyes closed (score = 3 for palm and score = 2 for thumb). In accordance with RASP scoring classification, on the basis of the number of correct scores, the sensation was classified as absent, impaired, or normal. After this preliminary visit, our patient was enrolled in the robot-training program and underwent the following assessments as part of the study protocol: (a) evaluation before (t0), after training (t1), and at 3month follow-up (t2) of standardized clinical scales; and (b) quantitative evaluation of the sense of position and six robot measured parameters at the start and end of training. In addition, robot-measured parameters were evaluated during each training session.
2.3. Clinical evaluation tools
Figure 1. Computer tomography scan sample image of patient described in this case report. The patient suffered from ischemic stroke in the territory of the leftmiddle cerebral artery.
The level of impairment of the patient’s right arm was evaluated by the upper-limb section of the FM assessment scale (FM range = 0–66) (Fugl-Meyer, Jääskö, Leyman, Olsson, & Steglind, 1975). Further, the FM assessment included
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evaluation of sensation with light touch at the volar side of the forearm and palmar surface of the hand, and position sense of the glenohumeral, elbow, wrist, and thumb (interphalangeal) joints (Platz, Pinkowski, van Wijck, & Johnson, 2005). Muscle tone and spasticity was evaluated by the modified Ashworth scale (MAS) (Bohannon & Smith, 1987). Muscle strength was evaluated by the muscular manual test (MMT) performed in standardized positions (Schwartz, Cohen, Herbison, & Shah, 1992). Finally, active movement joint limitation was evaluated by measuring shoulder, elbow, and wrist active ROM using a standard goniometer. All clinical assessments were carried out by trained therapists not involved in the training protocol.
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autonomously maintain a grasp, and for this reason, his hand was fastened to the robot-handle by a special strap. The left handle, the “active handle,” was independent of the robot and could be actively moved in the workspace by the subject using the intact (left) arm. The active- and passive-hand raw positions (i.e., the Cartesian position of the center of the handles) were artificially mirrored across the x-coordinate in correspondence to the midsagittal axis. In this way, the actual and desired positions would overlap in the case of perfect matching. In accordance with Dukelow et al. (2010), the sense of position was evaluated by computing the following quantitative parameters.
2.4.1. Variability
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2.4. Quantitative evaluation of proprioception Quantitative evaluation of the sense of position was carried out through a specific device consisting of the 2-DoF robot device “Braccio di Ferro” (Iron Arm) (Casadio, Sanguineti, Morasso, & Arrichiello, 2006), the same employed for robot therapy (Figure 2a), combined with a low-cost motion analysis system composed of a standard digital camera (Sony®, DCRPC1000E). The patient was blindfolded with a black tissue mask so as to prevent use of visual feedback during evaluation. He was comfortably seated at the robot with his midsagittal axis aligned with the center of the robot-workspace and grasped two handles, during a limb position-matching task. These handles had some colored markers that allowed measurement of the position of both upper limbs in the horizontal plane by analyzing the images captured by the digital camera. Details about the device and procedure can be found elsewhere (Cusmano et al., 2014). In the present study, the right handle was the “passive handle,” i.e., the robot-handle grasped with the impaired hand-limb and passively moved through the workspace. Due to the severity of hand impairment, the patient was unable to
(A)
It is obtained by computing the standard deviation of the active-hand positions for each target in the x-coordinate (Varx; note that Var stands for variability and not for variance) and y-coordinate (Vary), and the variability for both coordinates combined (Varxy)
2.4.2. Spatial contraction/expansion That is, the range/area of the workspace matched by the active (left) hand relative to that of the passive (right) one. The values below 1 were obtained when the range of space explored by the active hand was smaller than that obtained by the passive hand, so indicating a spatial contraction. Conversely, spatial expansion was indicated by the values above 1. The procedure was performed to compute the parameters along the x (Cont/Expx), y (Cont/Expy), and both coordinates (Cont/Expxy).
2.4.3. Systematic shifts We computed the systematic shifts for each target for the x-coordinate (Shiftx), y-coordinate (Shifty), and both coordinates (Shiftxy).
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Figure 2. Robot devices used for evaluation and training of the patient. (a) Device “Braccio di Ferro” during the evaluation of sense of position. (b) Wrist manipulator and (c) shoulder–elbow manipulator (“Braccio di Ferro”) during robot therapy.
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2.4.4. Mean error The mean value of the absolute differences between, respectively, the actual and the desired positions for the x (Errorx), y (Errory), and xy (Errorxy) coordinates. This parameter in reference to normative data should indicate the presence of proprioceptive deficits.
2.5. Quantitative evaluation during robot therapy At the start, end, and during robot-assisted treatment, consisting of a sequence of point-to-point reaching tasks with visual feedback (eyes open), we recorded the position of the robot end-effector at 100 Hz sampling rate. Then, we computed the following performance parameters (Colombo et al., 2008, 2014).
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2.5.1. Active movement index The active movement index (AMI) represents the patient’s ability to autonomously execute the requested motor task without any assistance of the robot. It is calculated as the percentage of trajectory travelled by means of the patient’s voluntary activity.
2.5.2. Mean velocity The mean value of the velocity of the robot end-effector measured during each reaching movement.
2.5.3. Movement accuracy It measures the mean absolute distance (MD) of each point of the actual path travelled by the subject from the theoretical path. When this parameter approximates zero, movement accuracy will be very high. Actually, it measures the error of accuracy; hence, a decrease in this index during training indicates an improvement of accuracy in the motor task execution.
2.5.4. Normalized path length The normalized path length (nPL) is obtained by computing the path length of the trajectory travelled by the patient to reach the target and normalized to the theoretical path. It estimates the error of movement efficiency; therefore, decreasing values during training reflect an improvement of efficiency in the motor task execution.
2.5.5. Movement smoothness This was estimated by two different parameters: (a) smoothness of the reaching movement (SM), computed as the ratio between the peak tangential speed and the mean speed of the robot end-effector (Bosecker, Dipietro, Volpe, & Krebs, 2010); and (b) spectral arc length (SAL), assessing the small oscillations in the trajectory speed profile that correspond to frequencies higher than the underlying movement (Balasubramanian, Melendez-Calderon, & Burdet, 2012). Both these parameters have been recently demonstrated to be reliable measures of movement smoothness (Colombo et al., 2014). The performance parameters were always measured during the voluntary activity (unassisted) phase of each reaching movement, and averaged so as to obtain for each parameter one mean value for each training session.
2.6. Training devices and rehabilitation intervention During his stay in our rehabilitation unit, the patient was treated using two robot devices: the 2-DoF elbow–shoulder manipulator “Braccio di Ferro” and 1-DoF wrist manipulator (Figure 2) (Casadio et al., 2006; Colombo et al., 2005). The end-effector of both devices consisted of a handle including a force sensor, which is grasped by the patient and moved through the respective workspace. The first one enabled the patient to execute reaching movements in the horizontal plane, the second allowed only training of flexion and extension of the wrist joint on the horizontal plane. The robot controller allowed execution both of completely voluntary movements and of “shared” controlled movements in which the device assisted the subject to complete the part of the task he was unable to do by means of voluntary effort. The assistance consisted of a constant force whose magnitude was set at the beginning of training in order to be able to passively drive the patient’s arm to the target in all the positions of the reachable workspace. It was gradually applied in a ramp-shape mode lasting 2 s, only when the patient was unable to autonomously complete the motor task. In practice, the patient started the task by trying to move the robot handle without any assistance. The robot controller continuously evaluated the current position of the handle and, if it remained for 3 s in the same place, then applied the assistance force so as to guide the patient’s arm to the target position (activity triggered assistance). During the training, depending on the robot device, the patient had to complete a motor task consisting of a sequence of pointto-point reaching movements in the shape of a geometrical figure (square) or a fully active or active-assisted wrist flexion and extension movement. Details of the administered tasks and procedures have been extensively reported before (Colombo et al., 2005, 2008). In this patient, a validated algorithm automatically changed the difficulty level of the task, by changing the square edge size from 150 mm to 220 mm at training session 9 based on his performance evaluated on the three robot-measured parameters AMI, mean velocity (MV), and nPL (Colombo, Sterpi, Mazzone, Delconte, & Pisano, 2012). The patient underwent the training twice a day, 5 days a week for 3½ weeks with the elbow– shoulder and wrist manipulators. Each training session consisted of four cycles of exercise lasting 5 min each followed by a 3 min resting period. On the same days as robot treatment, he underwent physical therapy including treatment for upper and lower limbs performed by professional therapists for 45 min/day, according to the Italian Stroke Prevention and Educational Awareness Diffusion (SPREAD) guidelines.
3. Outcomes The robot-assisted therapy was well accepted and tolerated by the patient who executed a total of 4864 reaching movements with the elbow–shoulder manipulator and 1484 extension– flexion movements with the wrist manipulator. On average, including resting periods and the time for connection/disconnection to the robots, our patient received 96 min/day of
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robot therapy for a total of 1536 min of treatment. No adverse events were observed during training.
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3.1. Clinical scale measures Table 1 reports the values of the clinical scales measured before and after training and at 3-month follow-up. The RASP assessment of sensation during the preliminary neurological visit showed that our patient had touch sensation absent and proprioception impaired. No clinical changes were observed at the end of training and follow-up in the FM scale, touch sensation, and position sense. The MAS was unchanged in the shoulder and wrist, and showed a 1-point reduction after training in the hand and elbow but which returned to the pretreatment value at follow-up. A slight improvement was observed in the MMT at the wrist joint and in elbow flexion. The active ROM of the shoulder was improved (15°) during flexion at the end of training, but returned to pretreatment values at follow-up. Only the wrist joint exhibited a slight improvement of wrist flexion (20°) that was maintained at follow-up.
Table 1. Clinical scale values measured before, after training and at 3-month follow-up. Clinical scale Fugl-Meyer (0–66) MAS
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Joint/Site Upper limb Shoulder Elbow Wrist Hand Shoulder Elbow Wrist Hand/Fingers Shoulder Elbow Wrist Forearm Hand-palm Glenohumeral Elbow Wrist Thumb
Pre 20 0 1 1 2 2−/3− 3+/4 1/1 2−/0 45°/40° 140°/180° 0°/0° Dysesthesia Dysesthesia Normal Normal Absent Absent
Post 20 0 0 1 1 2−/3 4/4 2−/1 2−/0 60°/40° 140°/180° 20°/0° Dysesthesia Dysesthesia Normal Normal Absent Absent
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3-month 20 0 1 1 1 2−/3 4/4 2−/1 2−/0 45°/40° 140°/180° 20°/0° Dysesthesia Dysesthesia Normal Normal Absent Absent
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3.2. Changes in sense of position after treatment Figure 3 reports the patient’s sense of position evaluated before (a) and after (b) 3 weeks of training with the robotic devices; in addition, the performance pattern of a healthy subject is included for comparison. Specifically, the black squares and solid connecting lines represent the workspace positions through which the impaired (right) hand was passively moved by the robot. The subject’s performance is graphically summarized by reporting with black points, for each target location, the active hand’s mean position mirrored across the x-coordinate with respect to the midsagittal axis. In addition, the overlapped confidence ellipse represents one standard deviation around the mean target position (within-subject variability). The dashed line represents the equivalent geometric shape obtained during the mirror positioning task. It is evident that the performance obtained after training is clearly different (improved) from that obtained before training but, nevertheless, different from that of a healthy subject who in practice provided a nice overlap between active and passive limb patterns. This result is reflected by the changes observed in the quantitative parameters reported in Table 2. In fact, even if the patient did not show significant changes in variability (i.e., consistency of the active hand positioning), the Shiftxy (Pre = 11.83; Post = 10.21) and Errorxy (Pre = 11.92; Post = 10.36) decreased after robot treatment, the change being mainly evident along the y-axis. The Contr/Expy (Pre = 0.25; Post = 0.53) and Contr/Expxy (Pre = 0.06; Post = 0.13) values were double the initial values, but only the Contr/Expy had an increment higher than its minimal detectable change (Cusmano et al., 2014). In addition, the last two columns of Table 2 report the 5th and 95th percentiles computed in accordance with normative values reported by Herter, Scott, and Dukelow (2014). It is worth nothing that these normative data were collected using the same experimental protocol but with a different device, which provided the position of the index fingertips instead of the hand center (grasping center), as in our case. In spite of this, the abovereported parameters exhibit values that are outside the normative range but which, after sensorimotor training, move toward the normal confidence limits.
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Healthy Subject
Figure 3. Changes in sense of position observed in our patient evaluated before (Pre) and after (Post) robot-assisted rehabilitation of the upper limb. Note that the pattern of points and dashed lines refers to mirror positioning performance of the unimpaired hand-arm and that the pattern improvement was obtained mainly in the vertical direction. However, both patterns are clearly different from that of a healthy subject.
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Table 2. Values obtained by the sense of position (SoP) parameters of our patient after stroke. SoP parameter Varx (cm) Vary (cm) Varxy (cm) Shiftx (cm) Shifty (cm) Shiftxy (cm) Cont/Expx Cont/Expy Cont/Expxy Errorx (cm) Errory (cm) Errorxy (cm)
Normality range Pre 2.40 1.83 3.05 −1.10 9.51 11.83 0.26 0.25 0.06 5.73 9.53 11.92
Post 2.81 2.10 3.53 0.14 7.82 10.21 0.25 0.53 0.13 5.76 7.90 10.36
Mean 2.92 1.33 3.27 0.48 −0.62 4.01 0.78 1.01 0.79 4.24 1.62 4.68
SD 0.76 0.33 0.78 3.75 1.19 1.80 0.17 0.07 0.19 1.66 0.57 1.58
5th Perc. 2.02 0.88 1.98 −6.50 −4.17 1.04 – – 0.53 – – 3.14
95th Perc. 4.61 2.00 4.29 7.24 3.61 8.19 – – 1.20 – – 8.28
3.3. Changes of motor performance after treatment For the sake of conciseness, we report here only the parameter values obtained with the shoulder and elbow manipulator. Visual inspection of the trajectories travelled by the patient at the start and end of training (Figure 4) shows that at the end of training, they were more stereotyped and much more similar to the theoretical pattern than at the beginning of training. In addition, the size of the shape (i.e., the edge of the square) was increased, signifying an increase in the difficulty level. Figure 5 reports two typical tangential speed profiles obtained during the execution of a single square task of the training sessions reported in Figure 4 (i.e., sessions 2 and 30). The number of peaks in the profile is directly related to movement smoothness. It is worth noting, for each square edge, the reduction of the number of peaks obtained at the end of training compared to the beginning, testifying an improvement of movement smoothness after training. In accordance with our previous work (Panarese, Colombo, Sterpi, Pisano, & Micera, 2012), different movement directions exhibit different
4. Discussion The original hypothesis of this single-case pilot study was that sensorimotor training provided by the robot devices could improve not only the motor performance of the impaired arm but also the impaired sensory performance thus allowing improvement of the patient’s proprioception and hence of sense of position. In other words, we sought a preliminary answer to the question if limb position sense can be trained in a patient after stroke, and if there is any evidence of improvements in spatial movement accuracy. This hypothesis was
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Except for Cont/Expxy, the mean values and standard deviations refer to six positionings for each of the nine targets of the evaluation task. The normality range mean and s.d. values reported for comparison are taken from a previously published study (Cusmano et al., 2014). The 5th and 95th percentile columns were computed according to normative values reported in the literature (Herter et al., 2014).
degrees of improvement in performance. Specifically, the left edge of the square involving shoulder extension exhibits the worst smoothness performance (more jerky movement) at the end of training. Data analysis for the assessment of motor performance improvement involved measuring the above parameters for each sequence (i.e., the square) of point-to-point reaching movements executed during the two daily training sessions of the rehabilitation program. Specifically, our patient could execute many reaching sequences during a training session: from a minimum of 24 to a maximum of 46 depending on the session number, difficulty level of the task, etc. Therefore, we were able to compare data obtained in the first three (pretreatment) and in the last three training sessions (post-treatment) using Student’s t-test for repeated measures. Table 3 reports the results of the PRE vs. POST comparison. All robot measured parameters significantly improved after training: MV, MD, nPL, and SAL were those that exhibited the most important changes. Figure 6 reports the time course of recovery of the AMI, MV, nPL, SM, and SAL parameters over 32 training sessions. AMI shows that, using visual feedback, from the beginning of training our patient could execute most of the requested motor tasks by voluntary activity (without need for assistance). In most of the other parameters, one can clearly see an exponential increasing/decreasing pattern of improvement typical of stroke recovery, in which the largest improvements are observed early after training onset and subsequently gradually taper off.
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Table 3. Mean values and standard deviations of the kinematic parameters at PRE and POST training and the results of the PRE vs. POST comparison. Kinematic parameter AMI (%) MV (mm/s) MD (mm) nPL (a.u.) SM (a.u.) SAL (a.u.)
PRE Mean 98.12 38.41 14.48 2.78 0.25 −3.97
POST SD 5.79 7.43 4.32 1.80 0.04 0.95
Mean 100.00 75.12 9.72 1.36 0.30 −2.22
t −2.81 −27.87 8.02 6.76 −8.32 −13.89
SD 0.00 7.91 2.59 0.15 0.03 0.27
p-Value
