An Enhanced Deep Learning Approach for Smartphone-Based Human Activity Recognition in IoHT

Abstract

AbstractHuman activity recognition (HAR) uses sensor-based technology to predict human activity using sensor-generated time-series data. According to recent studies, researchers have been drawn to the area of HAR as the use of mobile devices with various sensors has increased in several research areas in health care that includes the identification of gait abnormality in brain or neurological disorder subjects, designing techniques for clinical gait analyses of a disabled and elderly person, etc. HAR is especially essential on the Internet of healthcare things (IoHT) because of the increasing growth of the Internet of Things (IoT) technology incorporated in numerous smart products and wearable technology (such as smartwatches and smartphones) that have a significant impact on the life of human. A deep neural network (DNN) comprising a bidirectional gated recurrent unit (BiGRU) and two convolutional layers is proposed in this research. The model used could automatically extract and identify activity features using a few of the architecture parameters. The raw data obtained using smartphone sensors is sent into a two layer BiGRU followed by two CNN layers in the proposed architecture. The model’s performance is assessed using two publicly available datasets (UCI-HAR and WISDM). The observations indicate that the reliability and activity identification capability of the proposed model is better than earlier findings.KeywordsHuman activity recognitionIoHTDeep learningBidirectional gated recurrent unit (BiGRU)Convolutional neural network (CNN)