Rehago - A Home-Based Training App Using Virtual Reality to Improve Functional Performance of Stroke Patients with Mirror Therapy and Gamification Concept: A Pilot Study.

Stroke is one of the prevalent diseases which leads to functional disabilities such as hemiparesis or hemiplegia. It is common practice to treat patients with proper rehabilitation as early as possible for better prognosis after the onset of stroke. One of the effective therapeutic techniques for treating stroke patients is mirror therapy, which can potentially facilitate patients' motor function recovery through repetitive practice. "Rehago" is a software as medical device that implements the concept of mirror therapy in combination with gamified exercises into virtual reality (VR) to provide a home-based rehabilitation environment for stroke patients. In this study, 48 stroke patients completed the full course of intervention with Rehago and their functional performance of pre- and post-intervention was investigated. The intervention with Rehago was predefined as 30 minutes training per day, 5 days per week over a course of 6 weeks. The patient's progress was evaluated by their therapists every 14 days, with a baseline assessment before the intervention began. The results showed an average improvement of 5.54 points in the Functional Independence Measurement score, and an improvement of 7.13 points in the assessed quality-of-life score (EQ5D-5L). An improvement of the FIM score and the quality-of-life score in EQ5D-5L was observed, indicating it is beneficial to the patients using Rehago as a home-based rehabilitation tool.

Workplace activity classification from shoe-based movement sensors.

BACKGROUND: High occupational physical activity is associated with lower health. Shoe-based movement sensors can provide an objective measurement of occupational physical activity in a lab setting but the performance of such methods in a free-living environment have not been investigated. The aim of the current study was to investigate the feasibility and accuracy of shoe sensor-based activity classification in an industrial work setting. RESULTS: An initial calibration part was performed with 35 subjects who performed different workplace activities in a structured lab setting while the movement was measured by a shoe-sensor. Three different machine-learning models (random forest (RF), support vector machine and k-nearest neighbour) were trained to classify activities using the collected lab data. In a second validation part, 29 industry workers were followed at work while an observer noted their activities and the movement was captured with a shoe-based movement sensor. The performance of the trained classification models were validated using the free-living workplace data. The RF classifier consistently outperformed the other models with a substantial difference in in the free-living validation. The accuracy of the initial RF classifier was 83% in the lab setting and 43% in the free-living validation. After combining activities that was difficult to discriminate the accuracy increased to 96 and 71% in the lab and free-living setting respectively. In the free-living part, 99% of the collected samples either consisted of stationary activities or walking. CONCLUSIONS: Walking and stationary activities can be classified with high accuracy from a shoe-based movement sensor in a free-living occupational setting. The distribution of activities at the workplace should be considered when validating activity classification models in a free-living setting.