Compensatory Trunk Research Articles

Ipsilateral primary motor cortex and behavioral compensation after stroke: a case series study.

Arm motor recovery after stroke is mainly attributed to reorganization of the primary motor cortex (M1). While M1 contralateral to the paretic arm (cM1) is critical for recovery, the role of ipsilateral M1 (iM1) is still inconclusive. Whether iM1 activity is related to recovery, behavioral compensation, or both is still far from settled. We hypothesized that the magnitude of iM1 activity in chronic stroke survivors will increase ordecrease in direct proportion to the degree that movements of the paretic arm are compensated. Movement kinematics (VICON, Oxford Metrics) and functional MRI data (3T MR system) were collected in 11 patients before and after a 4-week training designed to improve motor control of the paretic arm and decrease compensatory trunk recruitment. Twelve matched controls underwent similar evaluations and training. Relationships between iM1 activity and trunk motion were analyzed. At baseline, patients exhibited increased iM1 activity (p = 0.001) and relied more on trunk movement (p = 0.02) than controls. These two variables were directly and significantly related in patients (r = 0.74, p = 0.01) but not in controls (r = 0.28, p = 0.4). After training, patients displayed a significant reduction in iM1 activity (p = 0.008) and a trend toward decreased trunk use (p = 0.1). The relationship between these two variables remained significant (r = 0.66, p = 0.03) and different from controls (r = 0.26, p = 0.4). Our preliminary results suggest that iM1 may play a role in compensating for brain damage rather than directly gaining control of the paretic arm. However, we recommend caution in interpreting these results until more work is completed.

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy.

Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the real-time monitoring of compensation in patients with stroke by using pressure distribution data and machine learning algorithms. Whether trunk compensation can be reduced by combining the online detection of compensation and haptic feedback of a rehabilitation robot was also investigated. Six patients with stroke did three forms of reaching movements while pressure distribution data were recorded as Dataset1. A support vector machine (SVM) classifier was trained with features extracted from Dataset1. Then, two other patients with stroke performed reaching tasks, and the SVM classifier trained by Dataset1 was employed to classify the compensatory patterns online. Based on the real-time monitoring of compensation, a rehabilitation robot provided an assistive force to patients with stroke to reduce compensations. Good classification performance (F1 score > 0.95) was obtained in both offline and online compensation analysis using the SVM classifier and pressure distribution data of patients with stroke. Based on the real-time detection of compensatory patterns, the angles of trunk rotation, trunk lean-forward and trunk-scapula elevation decreased by 46.95%, 32.35% and 23.75%, respectively. High classification accuracies verified the feasibility of detecting compensation in patients with stroke based on pressure distribution data. Since the validity and reliability of the online detection of compensation has been verified, this classifier can be incorporated into a rehabilitation robot to reduce trunk compensations in patients with stroke.

SEMG-Based Trunk Compensation Detection in Rehabilitation Training.

Stroke patients often use trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, which results in a reduced rehabilitation training effect. Detecting trunk compensations can improve the effect of rehabilitation training. This study investigates the feasibility of a surface electromyography-based trunk compensation detection (sEMG-bTCD) method. Five healthy subjects and nine stroke subjects with cognitive and comprehension skills were recruited to participate in the experiments. The sEMG signals from nine superficial trunk muscles were collected during three rehabilitation training tasks (reach-forward-back, reach-side-to-side, and reach-up-to-down motions) without compensation and with three common trunk compensations [lean-forward (LF), trunk rotation (TR), and shoulder elevation (SE)]. Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value (MAV), waveform length, and the fourth order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent TCD performance was achieved in healthy participants by using support vector machine (SVM) classifier (LF: accuracy = 94.0%, AUC = 0.97, F1 = 0.94; TR: accuracy = 95.8%, AUC = 0.99, F1 = 0.96; SE: accuracy = 100.0%, AUC = 1.00, F1 = 1.00). By using SVM classifier, TCD performance in stroke participants was also obtained (LF: accuracy = 74.8%, AUC = 0.90, F1 = 0.73; TR: accuracy = 67.1%, AUC = 0.85, F1 = 0.71; SE: accuracy = 91.3%, AUC = 0.98, F1 = 0.90). Compared with the methods based on cameras or inertial sensors, better detection performance was obtained in both healthy and stroke participants. The results demonstrated the feasibility of the sEMG-bTCD method, and it helps to prompt the stroke patients to correct their incorrect posture, thereby improving the effectiveness of rehabilitation training.

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

BackgroundCompensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms.MethodsEight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-and-down reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements.ResultsCross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1-score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements.ConclusionsGood classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

Kinematic profiles during activities of daily living in adults with traumatic brachial plexus injuries

BackgroundInjuries to the brachial plexus leave individuals with lasting effects in upper extremity motor function, even with successful surgical restoration of elbow flexion. Quantitatively describing independent patient function during activities of daily living utilizing motion analysis could aid in prioritization of secondary surgical targets, as well as serve as an outcome measure. This study explored the upper extremity kinematic profiles during activities of daily living in adults with brachial plexus injury. MethodsEight adult participants (4 subjects with brachial plexus injury, 4 healthy controls) completed activities of daily living during one motion capture setting. Trunk, shoulder, and elbow joint minima, maxima, and range of motion were calculated and compared between groups. Kinematic profiles over a motion cycle were compared between groups using statistical parametric mapping. FindingsSubjects with brachial plexus injuries had significantly greater trunk range of motion during feeding and dressing tasks compared to control subjects. This compensatory trunk motion was accompanied by limited shoulder external rotation demonstrated using conventional descriptors and statistical parametric mapping. InterpretationSignificant compensatory trunk motion is required to complete select activities of daily living in subjects with brachial plexus injury. Additionally, restoration of shoulder external rotation would be a beneficial secondary target of surgical restoration of motor function. These aspects should be considered in treatment planning, as they could impact patient outcomes. Combining conventional descriptors of patient motion (e.g. joint minima, maxima, and range of motion) with statistical parametric mapping can provide a rich description of patient compensations and limitations.

Continues online exercise monitoring and assessment system with visual guidance feedback for stroke rehabilitation

Exercise therapy is a conventional intervention for stroke rehabilitation. Performance monitoring and feedback have shown to further improve the outcome of exercise therapy. This paper proposes a vision based system for monitoring exercise therapy which consists of 3 components: online exercise recognition, exercise performance analysis, and automatic visual feedback generation. The Microsoft Kinect was used for data acquisition. The exercise recognition component utilizes Kinect joints to continuously recognize and track the exercises. Upon completion of each exercise, joint flexibility and compensatory trunk motions are extracted for performance analysis. The visual feedback is a virtual skeleton augmented on top of the Kinect skeleton which displays the correct exercise path during execution. The Kinect skeleton and exercise definitions were applied to a motion hierarchy and animated using forward kinematics. Two additional experiments were also conducted to find accurate methods for calculating joint flexibility based on ROM measurement and trunk representation. Several datasets were created for system design and evaluation: 336 exercise sequences for exercise recognition, 25 records for ROM measurement, and 63 records for finding a suitable trunk representation method and compensatory motion detection. System evaluations showed that each component of the system is capable of producing outputs with significant accuracy.

Assessment of Spinal Curvatures in Children With Upper Trunk Obstetrical Brachial Plexus Palsy.

Effects of upper trunk obstetrical brachial plexus palsy (OBPP) on the spinal curvature and relationship between the curvature degrees and upper limb movements have not been reported. Spinal Mouse was used to assess spinal curvature characteristics in the sagittal and frontal planes. Upper limb movements were assessed with using the Active Movement Scale in only study group. The degree of frontal plane curvatures was higher in the study group. In the OBPP group, 11 of 25 (44%) children had thoracal C-shaped scoliosis. Nine of these children had contralateral side scoliosis. Shoulder external rotation was inversely correlated with scoliosis angle. Moreover, shoulder external rotation and elbow flexion were lower in children with scoliosis in the OBPP group. Upper trunk OBPP may affect frontal plane alignment. Especially insufficient shoulder external rotation scores may lead to constitute thoracal scoliosis as a result of trunk compensation.

Impaired posture, movement preparation, and execution during both paretic and nonparetic reaching following stroke

Posture and movement planning, preparation, and execution of a goal-directed reaching movement are impaired in individuals with stroke. No studies have shown whether the deficits are generally impaired or are specific to the lesioned hemisphere/paretic arm. This study utilized StartReact (SR) responses elicited by loud acoustic stimuli (LAS) to investigate the preparation and execution of anticipatory postural adjustments (APAs) and reach movement response during both paretic and nonparetic arm reaching in individuals with stroke and in age-matched healthy controls. Subjects were asked to get ready after receiving a warning cue and to reach at a "go" cue. An LAS was delivered at -500, -200, and 0 ms relative to the go cue. Kinetic, kinematic, and electromyographic data were recorded to characterize APA-reach movement responses. Individuals with stroke demonstrated systemwide deficits in posture and in movement planning, preparation, and execution of APA-reach sequence as shown by significant reduction in the incidence of SR response and impaired APA-reach performance, with greater deficits during paretic arm reaching. Use of trunk compensation strategy as characterized by greater involvement of trunk and pelvic rotation was utilized by individuals with stroke during paretic arm reaching compared with nonparetic arm reaching and healthy controls. Our findings have implications for upper extremity and postural control, suggesting that intervention should include training not only for the paretic arm but also for the nonparetic arm with simultaneous postural control requirements to improve the coordination of the APA-reach performance and subsequently reduce instability while functional tasks are performed during standing. NEW & NOTEWORTHY Our study is the first to show that nonparetic arm reaching also demonstrates impairment in posture and movement planning, preparation, and execution when performed during standing by individuals with stroke. In addition, we found compensatory trunk and pelvic rotations were used during a standing reach task for the paretic arms. The findings have clinical implications for upper extremity and postural rehabilitation, suggesting that training should include the nonparetic arms and incorporate simultaneous postural control demands.

The FreeD module for the Lokomat facilitates a physiological movement pattern in healthy people \u2013 a proof of concept study

BackgroundA contralateral pelvic drop, a transverse rotation and a lateral translation of the pelvis are essential features of normal human gait. These motions are often restricted in robot-assisted gait devices. The optional FreeD module of the driven gait orthosis Lokomat (Hocoma AG, Switzerland) incorporates guided lateral translation and transverse rotation of the pelvis. It consequently should support weight shifting during walking. This study aimed to investigate the influence of the FreeD module on trunk kinematics and hip and trunk muscle activity.MethodsThirty- one healthy adults participated. A video analysis of their trunk movements was performed to investigate the lateral chest and pelvis displacement within the Lokomat (with and without FreeD), and this was compared to treadmill walking. Furthermore, surface electromyography (sEMG) signals from eight muscles were collected during walking in the Lokomat (with and without FreeD), on the treadmill, and overground. To compare the similarity of the sEMG patterns, Spearman’s correlation analyses were applied.ResultsWalking with FreeD elicited a significantly higher lateral pelvis displacement and a lower lateral chest displacement (relative to the pelvis) compared to walking with a fixated pelvis. No significant differences in the sEMG patterns were found for the Lokomat conditions (with and without FreeD) when comparing it to treadmill or overground walking.ConclusionsThe differences in pelvis displacement act as a proof of concept of the FreeD module. The reduction of relative lateral chest movement corresponds to a decrease in compensatory trunk movements and has its origin in allowing weight shifting through the FreeD module. Both Lokomat conditions showed very similar muscle activity patterns of the trunk and hip compared to overground and treadmill walking. This indicates that the Lokomat allows a physiological muscle activity of the trunk and hip during gait.

A virtual reality tool for measuring and shaping trunk compensation for persons with stroke: Design and initial feasibility testing.

Background: Compensatory movement, such as flexing the trunk during reaching, may negatively affect motor improvement during task-based practice for persons with stroke. Shaping, or incrementally decreasing, the amount of compensation used during rehabilitation may be a viable strategy with methods using virtual reality.Methods: A virtual reality tool was designed to (1) monitor upper extremity movement kinematics with an off-the-shelf motion sensor (Microsoft Kinect V2), (2) convert movements into control of widely available computer games, and (3) provide real-time feedback to shape trunk compensation. This system was tested for feasibility by a small cohort of participants with chronic stroke (n = 5) during a 1-h session involving 40 min of virtual reality interaction. Outcomes related to repetitions, compensation, movement kinematics, usability, motivation, and sense of presence were collected.Results: Participants achieved a very high dose of reaching repetitions (461 ± 184), with an average of 81% being successful and 19% involving compensatory trunk flexion. Participants rated the system as highly usable, motivating, engaging, and safe.Conclusions: VRShape is feasible to use as a tool for increasing repetition rates, measuring and shaping compensation, and enhancing motivation for upper extremity therapy. Future research should focus on software improvements and investigation of efficacy during a virtual reality-based motor intervention.

Kinect-based assessment of proximal arm non-use after a stroke

BackgroundAfter a stroke, during seated reaching with their paretic upper limb, many patients spontaneously replace the use of their arm by trunk compensation movements, even though they are able to use their arm when forced to do so. We previously quantified this proximal arm non-use (PANU) with a motion capture system (Zebris, CMS20s). The aim of this study was to validate a low-cost Microsoft Kinect-based system against the CMS20s reference system to diagnose PANU.MethodsIn 19 hemiparetic stroke individuals, the PANU score, reach length, trunk length, and proximal arm use (PAU) were measured during seated reaching simultaneously by the Kinect (v2) and the CMS20s over two testing sessions separated by two hours.ResultsIntraclass correlation coefficients (ICC) and linear regression analysis showed that the PANU score (ICC = 0.96, r2 = 0.92), reach length (ICC = 0.81, r2 = 0.68), trunk length (ICC = 0.97, r2 = 0.94) and PAU (ICC = 0.97, r2 = 0.94) measured using the Kinect were strongly related to those measured using the CMS20s. The PANU scores showed good test-retest reliability for both the Kinect (ICC = 0.76) and CMS20s (ICC = 0.72). Bland and Altman plots showed slightly reduced PANU scores in the re-test session for both systems (Kinect: − 4.25 ± 6.76; CMS20s: − 4.71 ± 7.88), which suggests a practice effect.ConclusionWe showed that the Kinect could accurately and reliably assess PANU, reach length, trunk length and PAU during seated reaching in post stroke individuals. We conclude that the Kinect can offer a low-cost and widely available solution to clinically assess PANU for individualised rehabilitation and to monitor the progress of paretic arm recovery.Trial registrationThe study was approved by The Ethics Committee of Montpellier, France (N°ID-RCB: 2014-A00395–42) and registered in Clinical Trial (N° NCT02326688, Registered on 15 December 2014, https://clinicaltrials.gov/ct2/show/results/NCT02326688).

Changes in Trunk Compensation and Reaching Performance as a Result of a Novel Virtual Reality–Based Intervention for People With Chronic Stroke

Abstract Date Presented 4/20/2018 A virtual reality (VR) tool designed to provide compensation-related feedback during reaching practice was implemented with a small cohort of people with stroke. Results show that this tool may be a novel way to reduce maladaptive compensatory movements for people with neuromotor conditions. Primary Author and Speaker: Matthew Foreman Contributing Authors: Jack R. Engsberg

Comparison of the Spinal Loads Produced by Carrying a Backpack and Carrying a Person Piggyback: Crossfit Training

Background of Study: The piggyback carry has recently become a more popular exercise through the emerging sport of CrossFit. Purpose: The purpose of this study was to determine any biomechanical differences that exist in the lumbar spine when carrying no load, a backpack, and a person on the back. Methods: Twelve 70+ kg male strength-trained athletes were recruited from local CrossFit affiliates. One child with a mass of 27 kg was recruited to be the piggybackpassenger for all participants. All participants and the guardian of the passenger signed an informed consent form. The participants walked three times over a force plate for each of three conditions: carrying no load, a 27 kg backpack, or a 27 kg passenger. Three Canon video cameras recorded each trial, and Dartfish Software was used to measure joint angles and gait parameters. Maximal trunk inclination angle, was used in a static lumbar spine model to calculate trunkmuscle torque and force, and lumbosacral joint reaction forces. Results: Both load conditions produced compensatory trunk flexion; trunk flexion increased from no load to piggybacking to backpacking. Mean values were determined for each participant for each variable, and these values were compared amongst the three conditions of no load, piggybacking, and backpacking. An alpha value of 0.05 was used. Conclusion: Due to the more extreme position of the trunk andgreater magnitude of torques, backpacking likely places the musculoskeletal system at more risk than does piggybacking.

Compensatory strategy between trunk-hip kinematics and reaction time following slip perturbation between subjects with and without chronic low back pain

Compensatory trunk and hip motions following slip perturbations may compromise the control of lumbopelvic movement. However, it is unclear how postural integration of the trunk and hips can be transferred to treadmill-induced slip perturbations in subjects with chronic low back pain (LBP). The purpose of this study was to investigate trunk reaction times and three-dimensional trunk-hip angle changes following a slip perturbation (duration: 0.12 sec, velocity: 1.37 m/sec, displacement: 8.22 cm) with a handheld task between subjects with and without chronic LBP. There were 23 subjects with LBP and 33 control subjects who participated in the study. The trunk reaction time was not significantly different between groups. However, the three-dimensional trunk-hip angle changes were significantly different following the perturbation. There were significant interactions between the body regions and three-dimensional angles between groups. There was a negative correlation between reaction time and trunk flexion in the LBP group. Overall, the LBP group demonstrated significantly reduced trunk flexion, which might be associated with reduced adaptability or a possible fear of avoidance strategy. Clinicians need to consider compensatory strategies to improve trunk flexibility following slip perturbations in subjects with chronic LBP.Mini abstract: Trunk reaction time and three-dimensional trunk-hip motions were compared between subjects with and without chronic low back pain (LBP). The control group demonstrated greater trunk flexion; however, the LBP group reduced trunk flexion to protect against further injuries following the novelty of the slip perturbation.

Proprioceptive and motor training using the high performance robotic device hunova: Protocol of a randomized, controlled trial in patients with lower limb post-traumatic conditions

Lower limb trauma can cause kinetic chains impairments that compromise quality of movement and postural stability A pilot study is now being conducted in INAIL Rehabilitation Center of Volterra using Hunova, a robotic system for lower limbs and core stability training and evaluation, to examine whether robot-assisted therapy is effective in motor control and gait performance recovery when compared to conventional rehabilitation programs. Hunova consists of two mobile platforms for exercising in both seated and upright mono and bipodalic conditions The device can operate in passive, active and assistive mode, detecting compensatory trunk movements with an inertial sensor and giving sensory feedback during exercise. The ongoing open randomized controlled trial foresees the enrolment of fifty-six patients with functional locomotor impairments following work-related injuries of the lower limbs Patients are randomly allocated to experimental or control group and receive a 3 weeks rehabilitation program and a pre- and post-treatment assessment including robotic, clinical and instrumental non-robotic evaluations. All participants undergo an individualized rehabilitation protocol, tailored to specific patient needs and injury characteristics; the experimental group perform instability and resistive training with Hunova; control subjects follow a training program using traditional non-robotic equipment Results Robotic therapy was well tolerated and easy to manage An interim analysis on 37 patients shows that both groups benefited the proposed training protocol and robotic therapy was not inferior to conventional rehabilitation; moreover, patients with distal lower extremity injury show a significantly greater improvement in statistic stability than patients with proximal lower extremity injury. Hunova allows to measure significant parameters of static and dynamic stability and can centralize a complex progression of exercises to recover trunk control and reactive balance after traumatic injuries Final results of the study will contribute to explore the rule of robotic therapy in orthopedic conditions.

Evaluation of different types of prosthetic hands on the shoulder compensatory movements

The use of myoelectric prosthetics in upper limb amputees introduced compensations on the residual shoulder and trunk which could be the cause of musculoskeletal disorders. Task/work involving movements in abduction of shoulder (> 60°) more than two hours per day in cumulative have been identified to be responsible of musculoskeletal disorders. The aim of the study is to evaluate the shoulder compensations in upper limb amputees with the use of the new myoelectric multi-articulated prosthetic hands compared to tri-digital hands during different functional tasks. Five unilateral trans-radial upper limb amputees (4 male, 1 female; age 45.6 ± 13.8 years old) participated in this prospective crossover study. Evaluations were realized by each participant with their own tri-digital myoelectric prosthesis and 2 multigrip prosthesis: I Limb Ultra ® (IL) without a wrist mobility and Michelangelo ® (MA) with a wrist mobility, after specific rehabilitation and one month of home use. Mean of internal rotation, mean of abduction shoulder, percent time in abduction higher than 60° and its extrapolation in hours were calculated by an optoelectronic system (VICON) for the both sides (amputated - non-amputated) during the Box and Block Test (BBT) and 2 common daily life tasks (cut meat, make a knot). Whatever the tasks, there is any significant difference in internal rotation with the different prosthetic devices. New types of grip of innovative prosthetic hands and wrist mobility seems to decrease shoulder abduction, considered as pathogenic compensations. However, the compensation strategy is different across patients, which involve a personalized analysis for his rehabilitation program and for the prosthetic device choice. Further studies are necessary to evaluate the impact of trunk compensations.

Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices

BackgroundEnd-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient’s hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm.MethodsThe proposed algorithm is based on the inverse of the augmented Jaciobian as the algorithm (Papaleo, et al., Med Biol Eng Comput 53(9):815–28, 2015). However, the estimation of the elbow joint location is performed through the computation of the rotation measured by the accelerometer during the arm movement, making the algorithm more robust against shoulder movements. Furthermore, we present a method to compute the initial configuration of the upper limb necessary to start the integration method, a protocol to manually measure the upper arm and forearm lengths, and a shoulder position estimation. An optoelectronic system was used to test the accuracy of the proposed algorithm whilst healthy subjects were performing upper limb movements holding the end effector of the seven Degrees of Freedom (DoF) robot. In addition, the previous and the proposed algorithms were studied during a neuro-rehabilitation therapy assisted by the ‘PUPArm’ planar robot with three post-stroke patients.ResultsThe proposed algorithm reports a Root Mean Square Error (RMSE) of 2.13cm in the elbow joint location and 1.89cm in the wrist joint location with high correlation. These errors lead to a RMSE about 3.5 degrees (mean of the seven joints) with high correlation in all the joints with respect to the real upper limb acquired through the optoelectronic system. Then, the estimation of the upper limb joints through both algorithms reveal an instability on the previous when shoulder movement appear due to the inevitable trunk compensation in post-stroke patients.ConclusionsThe proposed algorithm is able to accurately estimate the human upper limb joints during a neuro-rehabilitation therapy assisted by end-effector robots. In addition, the implemented protocol can be followed in a clinical environment without optoelectronic systems using only one accelerometer attached in the upper arm. Thus, the ROM can be perfectly determined and could become an objective assessment parameter for a comprehensive assessment.

Can We Achieve Intuitive Prosthetic Elbow Control Based on Healthy Upper Limb Motor Strategies?

Most transhumeral amputees report that their prosthetic device lacks functionality, citing the control strategy as a major limitation. Indeed, they are required to control several degrees of freedom with muscle groups primarily used for elbow actuation. As a result, most of them choose to have a one-degree-of-freedom myoelectric hand for grasping objects, a myoelectric wrist for pronation/supination, and a body-powered elbow. Unlike healthy upper limb movements, the prosthetic elbow joint angle, adjusted prior to the motion, is not involved in the overall upper limb movements, causing the rest of the body to compensate for the lack of mobility of the prosthesis. A promising solution to improve upper limb prosthesis control exploits the residual limb mobility: like in healthy movements, shoulder and prosthetic elbow motions are coupled using inter-joint coordination models. The present study aims to test this approach. A transhumeral amputated individual used a prosthesis with a residual limb motion-driven elbow to point at targets. The prosthetic elbow motion was derived from IMU-based shoulder measurements and a generic model of inter-joint coordinations built from healthy individuals data. For comparison, the participant also performed the task while the prosthetic elbow was implemented with his own myoelectric control strategy. The results show that although the transhumeral amputated participant achieved the pointing task with a better precision when the elbow was myoelectrically-controlled, he had to develop large compensatory trunk movements. Automatic elbow control reduced trunk displacements, and enabled a more natural body behavior with synchronous shoulder and elbow motions. However, due to socket impairments, the residual limb amplitudes were not as large as those of healthy shoulder movements. Therefore, this work also investigates if a control strategy whereby prosthetic joints are automatized according to healthy individuals' coordination models can lead to an intuitive and natural prosthetic control.

Effect of task-oriented training with and without trunk Restraint on Reaching Activity in Adult Hemiparetics

The normal pattern for reaching to a target is not seen in hemiparesis, as patients are seen to use compensatory trunk movements to accomplish the same task. As much as this compensatory behavior allows them to accomplish the task regardless of the motor deficit, it however, may not be desirable for skill requisition. Past studies have shown that limiting these compensatory movements effectively improves reaching activity in adult hemiparetics. However in those studies, the investigators analyzed the effects of training for a very brief period. Thus in this study we are aiming to determine the effect of task-oriented training with trunk restraint on reaching activity in adult hemiparetics, where 20 adult hemiparetics over the age of 40 years were recruited and were randomly allocated to control (n=10) and experimental (n=10) groups. Participants of both groups were assigned a reaching task for 60 repetitions in a single session in 3 directions for 10 sessions within 2 weeks period. Trunk restraint was introduced to the experimental group while performing the reaching task. Pre and post-intervention scores of the Reaching Performance Scale (RPS) were analyzed. Participants of both the groups show significant (p< 0.05) change in RPS scores. Inter group post intervention RPS scores show a significant (p<0.05) differences. Additionally, when the mean values between the groups were compared, the post intervention RPS scores of the experimental groups were higher than the control group, implying better performance in the former.

Does motivation matter in upper-limb rehabilitation after stroke? ArmeoSenso-Reward: study protocol for a randomized controlled trial

BackgroundFifty percent of all stroke survivors remain with functional impairments of their upper limb. While there is a need to improve the effectiveness of rehabilitative training, so far no new training approach has proven to be clearly superior to conventional therapy. As training with rewarding feedback has been shown to improve motor learning in humans, it is hypothesized that rehabilitative arm training could be enhanced by rewarding feedback. In this paper, we propose a trial protocol investigating rewards in the form of performance feedback and monetary gains as ways to improve effectiveness of rehabilitative training.MethodsThis multicentric, assessor-blinded, randomized controlled trial uses the ArmeoSenso virtual reality rehabilitation system to train 74 first-ever stroke patients (< 100 days post stroke) to lift their impaired upper limb against gravity and to improve the workspace of the paretic arm. Three sensors are attached to forearm, upper arm, and trunk to track arm movements in three-dimensional space while controlling for trunk compensation. Whole-arm movements serve as input for a therapy game. The reward group (n = 37) will train with performance feedback and contingent monetary reward. The control group (n = 37) uses the same system but without monetary reward and with reduced performance feedback. Primary outcome is the change in the hand workspace in the transversal plane. Standard clinical assessments are used as secondary outcome measures.DiscussionThis randomized controlled trial will be the first to directly evaluate the effect of rewarding feedback, including monetary rewards, on the recovery process of the upper limb following stroke. This could pave the way for novel types of interventions with significantly improved treatment benefits, e.g., for conditions that impair reward processing (stroke, Parkinson’s disease).Trial registrationClinicalTrials.gov, ID: NCT02257125. Registered on 30 September 2014.