Background. Embedding mirror therapy within a virtual reality (VR) system may have a superior effect on motor remediation for chronic stroke patients. Objective. The objective is to investigate the differences in the effects of using conventional occupational therapy (COT), mirror therapy (MT), and VR-based MT (VR-MT) training on the sensorimotor function of the upper limb in chronic stroke patients. Methods. This was a single-blinded randomized controlled trial. A total of 54 participants, including chronic stroke patients, were randomized into a COT, MT, or VR-MT group. In addition to 20-minute sessions of task-specific training, patients received programs of 30 minutes of VR-MT, 30 minutes of MT, and 30 minutes of COT, respectively, in the VR-MT, MT, and COT groups twice a week for 9 weeks. The Fugl-Meyer motor assessment for the upper extremities (FM-UE; primary outcome), Semmes-Weinstein monofilament, motor activity log, modified Ashworth scale, and the box and block test were recorded at pre-treatment, post-intervention, and 12-week follow-up. Results. Fifty-two participants completed the study. There was no statistically significant group-by-time interaction effects on the FM-UE score (generalized estimating equations, (GEE), P = .075). Meanwhile, there were statistically significant group-by-time interaction effects on the wrist sub-score of the FM-UE (GEE, P = .012) and the result of box and block test (GEE, P = .044). Conclusions. VR-MT seemed to have potential effects on restoring the upper extremity motor function for chronic stroke patients. However, further confirmatory studies are warranted for the rather weak evidence of adding VR to MT on improving primary outcome of this study. Clinical trial registration: NCT03329417.

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.

This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.

To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable—particularly in the early stages of the diseases. This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data. The irregular gait patterns of NDDs exhibited by vGRF data can indicate different variations of force patterns compared with healthy controls (HC). The classification algorithm in this study comprises three processes: a preprocessing, feature transformation and classification. In the preprocessing process, the 5-min vGRF data divided into 10-s successive time windows. In the feature transformation process, the time-domain vGRF data are modified into an image using a recurrence plot. The total recurrence plots are 1312 plots for HC (16 subjects), 1066 plots for ALS (13 patients), 1230 plots for PD (15 patients) and 1640 plots for HD (20 subjects). The principal component analysis (PCA) is used in this stage for feature enhancement. Lastly, the convolutional neural network (CNN), as a deep learning classifier, is employed in the classification process and evaluated using the leave-one-out cross-validation (LOOCV). Gait data from HC subjects and patients with amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD) and Parkinson’s disease (PD) obtained from the PhysioNet Gait Dynamics in Neurodegenerative disease were used to validate the proposed algorithm. The experimental results included two-class and multiclass classifications. In the two-class classification, the results included classification of the NDD and the HC groups and classification among the NDDs. The classification accuracy for (HC vs. ALS), (HC vs. HD), (HC vs. PD), (ALS vs. PD), (ALS vs. HD), (PD vs. HD) and (NDDs vs. HC) were 100%, 98.41%, 100%, 95.95%, 100%, 97.25% and 98.91%, respectively. In the multiclass classification, a four-class gait classification among HC, ALS, PD and HD was conducted and the classification accuracy of HC, ALS, PD and HD were 98.99%, 98.32%, 97.41% and 96.74%, respectively. The proposed method can achieve high accuracy compare to the existing results, but with shorter length of input signal (Input of existing literature using the same database is 5-min gait signal, but the proposed method only needs 10-s gait signal).

Mirror therapy (MT) has been proposed as an essential component of upper limb neurorehabilitation, using mirror illusion of the unaffected hand movement and superimposing it on the affected hand to improve neuroplasticity for improving motor recovery in stroke patients. In this paper, we developed a virtual reality mirror therapy (VRMT) system, and examined the performance of the proposed VRMT system. The VRMT system consists of a motion-tracking device, a VR goggle, and a VRMT software. Young, healthy participants, and stroke patients with mild-to-severe hemiparesis were recruited in this study, to validate the effectiveness of the proposed VRMT system. Experimental results based on the pinch-holding-up activity (PHUA) test revealed a significant effect on the precision pinch performance of young-healthy participants receiving VRMT, whereas traditional MT could not improve the precision pinch performance in young-healthy participants (Wilcoxon signed-rank test, Z = -2.693, p = 0.007). For patients having suffered a stroke and being treated with VRMT, significant beneficial effects examined by mixed effect model were found on the total score of FMA (p = 0.033) and hand part of FMA (p = 0.008). The findings in this study indicate that the VRMT system has a potentially positive effects on the sensorimotor performance of hands in healthy participants. In addition, add-on effect of VR to MT provides beneficial effects on motor function of the upper extremity in chronic stroke patients.