Performance of End-to-end Model Based on Convolutional LSTM for Human Activity Recognition

• Published in: Journal of web engineering Vol. 21; no. 5; p. 1671
• Main Authors: Sun, Young Ghyu, Kim, Soo Hyun, Lee, Seongwoo, Seon, Joonho, Lee, SangWoon, Kim, Cheong Ghil, Kim, Jin Young
• Format: Journal Article
• Language: English
• Published: Milan River Publishers 01.01.2022
• Subjects: Datasets; Deep learning; Human activity recognition; Machine learning; Sensors; Smart buildings; Video data
• ISSN: 1540-9589; 1544-5976
• DOI: 10.13052/jwe1540-9589.21512

Human activity recognition (HAR) is a key technology in many applications, such as smart signage, smart healthcare, smart home, etc. In HAR, deep learning-based methods have been proposed to recognize activity data effectively from video streams. In this paper, the end-to-end model based on convolutional long short-term memory (LSTM) is proposed to recognize human activities. Convolutional LSTM can learn features of spatial and temporal simultaneously from video stream data. Also, the number of learning weights can be diminished by employing convolutional LSTM with an end-to-end model. The proposed HAR model was optimized with various simulation environments using activities data from the AI hub. From simulation results, it can be confirmed that the proposed model can be outperformed compared with the conventional model.