HUMAN ACTIVITY RECOGNITION USING LONG SHORT-TERM MEMORY NETWORK

Human Activity Recognition (HAR) plays a significant role in the Ambient Assisted Living (AAL) system, which aims to provide sustainable healthcare for an aging population and those with special needs. HAR automatically categorizes people’s activi ties while they wear wearable sensors. With an effective HAR system, we should be able to monitor the behavior of individuals as well as their activities and issue specific warn ings as necessary. The goal of this paper is to propose a methodological framework for developing the HAR model based on an application of Long Short-Term Memory (LSTM) network. We investigated the model selection and parameters based on Cross Validation (CV) and learning rate optimization across two well-known public HAR datasets, Mo biAct and WISDM. An analysis of the CV variance becomes a considerable impact on the generalization of the model’s learning capability. The relationship between the CV variance and accuracy can be used to guide the selection of the fold number in k-fold CV. Our studies had shown the scientific evidence and technical guidance for solving the HAR problem with improvements not only in the proposed model’s accuracy and AUC of more than 99% on average, but also in its generalization performance, which could be useful for future related studies.