Abstract
This study developed a virtual reality interactive game with smart wireless wearable technology for healthcare of elderly users. The proposed wearable system uses its intelligent and wireless features to collect electromyography signals and upload them to a cloud database for further analysis. The electromyography signals are then analyzed for the users’ muscle fatigue, health, strength, and other physiological conditions. The average slope maximum So and Chan (ASM S & C) algorithm is integrated in the proposed system to effectively detect the quantity of electromyography peaks, and the accuracy is as high as 95%. The proposed system can promote the health conditions of elderly users, and motivate them to acquire new knowledge of science and technology.
Highlights
Open-source software (OSS), this study developed an interesting virtual reality (VR) digital game that could stimulate the elderly users to utilize the application for health management [13,14,15]
The signalgenerated generated bythe thepotential potential difference between both muscle signal by difference between this paper, disposable electrodes were attached to the skin to observe muscle activities
The experiment data the were the muscle states making a fist second within one experiment dataexperiment were muscle states of making a fist once peronce second one minute, disposable electrodes were attached to the flexor digitorum superficialis muscle (FDS)
Summary
The biomedical VR games, which are characterized by low complexity [19,20] This proposed system targets therealization irregularities of EMG peaks, in order to improve study aims to develop an innovative architecture with low complexity. The the operational accuracy and smoothness of VR games.ofToEMG verify the EMG peaktodetection proposed system targets the irregularities peaks, in order improve accuracy, this study first used automaticof calibration detection (ACD). To verify the EMG peak detection signals, and setstudy a threshold parameter to assess the captureddetection signals To further accuracy, this first used the automatic calibration toimprove capture the the peak detection accuracy, this study applied the average slope maximum. Accuracy, The comparison of recent studies on physiological signal prove Sthe peak detection this study applied the average slope maximum detection systems is as shown in.
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