Why the study?
Human activity recognition has applications in telemedicine and rehabilitation, and advances in deep learning allow for more accurate classifiers using small, raw datasets from customized footwear.
Artificial neural network based software techniques can accurately classify human activity using small datasets and low-cost electronic hardware.
May support low-cost activity monitoring in cardiac patients; hypothesis-generating pending clinical validation.
Human Activity recognition has many potential applications in telemedicine and rehabilitation. The advent and recent improvements in deep learning have encouraged the development of more accurate classifiers that were previously unprecedented. The present work focuses on classifying human activity using small, raw datasets collected with instrumented and customized footwear. Multi-channel time series data were recorded and transmitted wirelessly to a recurrent neural network classifier, which was able to classify 6 activities with an accuracy of up to 87.0 ± 8.9%. Lower level features were detected by the use of convolution. The present work shows that it is possible to use artificial neural network based software techniques to accurately classify data, even with small datasets and low-cost electronic hardware.
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Paydarfar et al. (2020) studied this question.
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