Robotic therapies may be useful during the subacute stages of stroke - both endpoints (FM hand and MI prehension) showed the expected trend with bigger effect size for the robotic intervention. Additional benefit of the robotic therapy over the control therapy was only significant when the difference was measured with FM, demanding further investigation with larger samples. Implications of this study are important for decision making during therapy administration and resource allocation.

Prefrontal cortex and cerebellar activity are the driving forces of the recovery associated with Gesture Therapy. The relation between behavioral and brain changes suggests that those with stronger impairment benefit the most from this paradigm.

Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength-relevant for rehabilitation-. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.

BackgroundRobotic arm therapy devices that incorporate actuated assistance can enhance arm recovery, motivate patients to practice, and allow therapists to deliver semi-autonomous training. However, because such devices are often complex and actively apply forces, they have not achieved widespread use in rehabilitation clinics or at home. This paper describes the design and pilot testing of a simple, mechanically passive device that provides robot-like assistance for active arm training using the principle of mechanical resonance.MethodsThe Resonating Arm Exerciser (RAE) consists of a lever that attaches to the push rim of a wheelchair, a forearm support, and an elastic band that stores energy. Patients push and pull on the lever to roll the wheelchair back and forth by about 20 cm around a neutral position. We performed two separate pilot studies of the device. In the first, we tested whether the predicted resonant properties of RAE amplified a user’s arm mobility by comparing his or her active range of motion (AROM) in the device achieved during a single, sustained push and pull to the AROM achieved during rocking. In a second pilot study designed to test the therapeutic potential of the device, eight participants with chronic stroke (35 ± 24 months since injury) and a mean, stable, initial upper extremity Fugl-Meyer (FM) score of 17 ± 8 / 66 exercised with RAE for eight 45 minute sessions over three weeks. The primary outcome measure was the average AROM measured with a tilt sensor during a one minute test, and the secondary outcome measures were the FM score and the visual analog scale for arm pain.ResultsIn the first pilot study, we found people with a severe motor impairment after stroke intuitively found the resonant frequency of the chair, and the mechanical resonance of RAE amplified their arm AROM by a factor of about 2. In the second pilot study, AROM increased by 66% ± 20% (p = 0.003). The mean FM score increase was 8.5 ± 4 pts (p = 0.009). Subjects did not report discomfort or an increase in arm pain with rocking. Improvements were sustained at three months.ConclusionsThese results demonstrate that a simple mechanical device that snaps onto a manual wheelchair can use resonance to assist arm training, and that such training shows potential for safely increasing arm movement ability for people with severe chronic hemiparetic stroke.

Abstract-Virtual rehabilitation supports motor training following stroke by means of tailored virtual environments. To optimize therapy outcome, virtual rehabilitation systems automatically adapt to the different patients' changing needs. Adaptation decisions should ideally be guided by both the observable performance and the hidden mind state of the user. We hypothesize that some affective aspects can be inferred from observable metrics. Here we present preliminary results of a classification exercise to decide on 4 states; tiredness, tension, pain and satisfaction. Descriptors of 3D hand movement and finger pressure were collected from 2 post-stroke participants while they practice on a virtual rehabilitation platform. Linear Support Vector Machine models were learnt to unfold a predictive relation between observation and the affective states considered. Initial results are promising (ROC Area under the curve (meanstd): 0.713  0.137). Confirmation of these opens the door to incorporate surrogates of mind state into the algorithm deciding on therapy adaptation.