BackgroundSeveral strategies have been proposed to improve patient motivation and exercise intensity during robot-aided stroke rehabilitation. One relatively unexplored possibility is two-player gameplay, allowing subjects to compete or cooperate with each other to achieve a common goal. In order to explore the potential of such games, we designed a two-player game played using two ARMin arm rehabilitation robots.MethodsThe game was an air-hockey task displayed on a computer monitor and controlled using shoulder movements in the ARMin robot. Three game modes were tested: single-player (competing against computer), competitive (competing against human), and cooperative (cooperating with human against computer). All modes were played by 30 unimpaired subjects and 8 impaired chronic stroke subjects. The subjects filled out the Intrinsic Motivation Inventory questionnaire after each game mode, as well as a final questionnaire about game preferences and their personality.ResultsNearly all unimpaired subjects preferred playing the two-player game modes to the single-player one, as they enjoyed talking and interacting with another person. However, there were two distinct player groups: one liked the competitive mode but not the cooperative mode while the other liked the cooperative but not the competitive mode. Unimpaired subjects who liked the competitive mode also put significantly more effort into it than into the other modes. Results from impaired subjects were similar, with even impaired subjects over 60 years old enjoying competitive gameplay. The subjects’ personalities roughly predicted which mode they would prefer, which was especially evident in a poorly-matched impaired pair that preferred the single-player mode.ConclusionsResults indicate great potential for two-player rehabilitation games, in the form of greater enjoyment as well as potentially more intensive exercise compared to single-player games. However, the right game type needs to be chosen for each subject depending on skill and personality, along with selecting an appropriate co-player. Further studies with patients that are currently enrolled in rehabilitation programs are recommended, and the subjective measures used in our study should be augmented with objective measures such as electromyography.

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Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

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