Multi-input CNN-GRU based human activity recognition using wearable sensors

• Published in: Computing Vol. 103; no. 7; pp. 1461 - 1478
• Main Authors: Dua, Nidhi, Singh, Shiva Nand, Semwal, Vijay Bhaskar
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.07.2021; Springer Nature B.V
• Subjects: Accelerometers; Artificial Intelligence; Artificial neural networks; Classification; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Feature extraction; Human activity recognition; Information Systems Applications (incl.Internet); Moving object recognition; Neural networks; Regular Paper; Sensors; Software Engineering; Wearable technology; Long short term memory (LSTM); CNN; Deep neural networks; 68W; Primary Classification: 68T; GRU; Human activity recognition; 62H; 15A
• ISSN: 0010-485X; 1436-5057
• DOI: 10.1007/s00607-021-00928-8

Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope and accelerometer is in the form of time series data, as each reading has a timestamp associated with it. For HAR, it is important to extract the relevant temporal features from raw sensor data. Most of the approaches for HAR involves a good amount of feature engineering and data pre-processing, which in turn requires domain expertise. Such approaches are time-consuming and are application-specific. In this work, a Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well. The experiments in this work were carried out using the raw data obtained from wearable sensors with nominal pre-processing and don’t involve any handcrafted feature extraction techniques. The accuracies obtained on UCI-HAR, WISDM, and PAMAP2 datasets are 96.20%, 97.21%, and 95.27% respectively. The results of the experiments establish that the proposed model achieved superior classification performance than other similar architectures.