A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care

Abstract

AbstractNowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods.KeywordsHuman activity recognitionDeep learningIoHTConvolutional neural networks (CNN)Bidirectional long short-term memory networks (Bi-LSTM)