A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data

• Published in: The Visual computer Vol. 38; no. 12; pp. 4095 - 4109
• Main Authors: Challa, Sravan Kumar, Kumar, Akhilesh, Semwal, Vijay Bhaskar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Computer Graphics; Computer Science; Datasets; Deep learning; Feature extraction; Human activity recognition; Image Processing and Computer Vision; Machine learning; Neural networks; Original Article; Performance evaluation; Sensors; Smartphones; Time series; Ubiquitous computing; Unmanned aerial vehicles; Wearable technology; HAR; Wearable sensor data; CNN; Deep neural networks; Bidirectional LSTM

Human activity recognition (HAR) has become a significant area of research in human behavior analysis, human–computer interaction, and pervasive computing. Recently, deep learning (DL)-based methods have been applied successfully to time-series data generated from smartphones and wearable sensors to...