OPTIMIZING PHYSICAL ACTIVITY RECOGNITION USING HYBRID LSTM NETWORK

Abstract

Human Activity Recognition plays a crucial role in society. Due to the quick advancement of sensors such as smartwatches and other wearable devices recognizing human actions (HAR) have recently become more popular. The findings of significant HAR research projects are currently being applied in several mobile apps, such as health monitoring and athletic performance tracking, among others. Mainly, Human movement detection using sensors allows to predicting a person's movements using sensor-generated time- series data. In this paper, a HAR framework that uses automatically generated and is proposed to extract spatial-temporal information from data from smartwatch sensors. and also, we propose a Long Short-Term Memory Network (LSTM) and the 2D Convolutional Neural Network (2D-CNN) are employed in the framework to implement the hybrid deep learning approach, doing away with the necessity for feature extraction by manually. In this approach recognize the activities by considering both the significance of incoming short-term sensor data and the continuation of previous long-term sensor data activities. The outcomes showed the proposed deep learning hybrid LSTM model is more effective than the baseline models with an accuracy level of 91%. With an average improvement of more than 6% on the accuracy over the prior most effective model, we show that our suggested architecture that focused on attention is significantly more effective than prior approaches.