Kun Fang, Julong Pan*, Lingyi Li, Ruihan Xiang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008
Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance problems. Furthermore, a quantile-based approach… More >
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