Human Activity Recognition Based on Residual Network and BiLSTM

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 635
• Main Authors: Li, Yong, Wang, Luping
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Accuracy; Analysis; BiLSTM; Cameras; Classification; Datasets; Deep Learning; Human Activities; human activity recognition; Humans; inertial measurement unit; Machine learning; Neural networks; Neural Networks, Computer; Older people; Physical fitness; Rehabilitation; residual network; Running; Sensors; Smartphones; Support vector machines; Time series; Walking; inertial measurement unit; residual network; BiLSTM; human activity recognition
• DOI: 10.3390/s22020635
• ISSN: 1424-8220

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.