A Novel Wgf-ln Based Edge Driven Intelligence For Wearable Devices In Human Activity Recognition

This article discusses a 2020 study by Zhou and colleagues that demonstrated how human activity recognition with DL enhancements can aid IoT medical devices. A classification system based on long short-term memory (LSTM) was created to recognise granular patterns in sequential motion data. Maswadi also recommended Decision Tree and Naive Bayes classifiers for classifying human activities. The overall survey of wearable devices in human activity recognition with their methods and accuracy are presented in Table 1. A web-based, open-access IRS database is kept up by the Communications, Sensing, and Imaging group at the University of Glasgow in the United Kingdom.