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• W2057348344 abstract "Bilateral arm training is a popular upper extremity exercise for patients after stroke. Conventional bilateral arm trainers such as sanding units are simply made by woods, and they cannot provide quantitative performance data for automatic assessments. Hence, continuous investigations are generally required for such reciprocal operations, and those procedures result in extra loads of caregivers. In this paper, wireless sensor network (WSN) approaches are employed to develop automatic sensor data collections and sensor data intra-communications so that rehabilitation data could be properly collected. Three WSN motes are deployed in this system. Two motes are used for a set of sanding hand blocks, and the remaining mote is used for the reciprocal exercise platform. The most important feature of our approach is that three motes can be easily attached to conventional sanding units with less wiring and setup efforts to fast realize -plus functions. In addition to the collections of kinematic performance, the accelerations, grip force, applied weight, and the angle of upper plank are also automatically collected. The collected data is further transformed as rehabilitation performance indices (RPI) to guide the users to maximize their achievements, as well as to provide rehabilitation records for diagnoses and assessments. Finally, a system prototype is produced in this project to validate our approaches." @default.
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• W2057348344 date "2012-05-01" @default.
• W2057348344 modified "2023-09-26" @default.
• W2057348344 title "$e$-Plus Solutions for a Bilateral Arm Trainer With Wireless Sensor Networks" @default.
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• W2057348344 doi "https://doi.org/10.1109/jsen.2011.2167505" @default.
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