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

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 d...