Affiliation:
1. School of Computer and Information, Hohai University, Nanjing 211106, China
2. Nanjing Huiying Electronic Technology Co., Ltd., Nanjing 211100, China
Abstract
Recently, human action recognition has gained widespread use in fields such as human–robot interaction, healthcare, and sports. With the popularity of wearable devices, we can easily access sensor data of human actions for human action recognition. However, extracting spatio-temporal motion patterns from sensor data and capturing fine-grained action processes remain a challenge. To address this problem, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The spatial information of sensor signals is extracted by hierarchical multi-scale convolution with finer-grained features, and the multi-channel features are fused by adaptive channel feature fusion to retain important information and improve the efficiency of the model. The dynamic channel-selection-LSTM based on the attention mechanism captures the temporal context information and long-term dependence of the sensor signals. Experimental results show that the proposed model achieves Macro F1-scores of 0.68, 0.91, 0.53, and 0.96 on four public datasets: Opportunity, PAMAP2, USC-HAD, and Skoda, respectively. Our model demonstrates competitive performance when compared to several state-of-the-art approaches.
Funder
Key Research and Development Program of China
Key Research and Development Program of China, Yunnan Province
Fundamental Research Funds for the Central Universities
Postgraduate Research & Practice Innovation Program of Jiangsu Province
the 14th Five-Year Plan for Educational Science of Jiangsu Province
Jiangsu Higher Education Reform Research Project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference58 articles.
1. Anagnostis, A., Benos, L., Tsaopoulos, D., Tagarakis, A., Tsolakis, N., and Bochtis, D. (2021). Human activity recognition through recurrent neural networks for human–robot interaction in agriculture. Appl. Sci., 11.
2. Online human activity recognition employing hierarchical hidden Markov models;Asghari;J. Ambient Intell. Humaniz. Comput.,2020
3. Ramos, R.G., Domingo, J.D., Zalama, E., Gómez-García-Bermejo, J., and López, J. (2022). SDHAR-HOME: A sensor dataset for human activity recognition at home. Sensors, 22.
4. Mobile phone sensing systems: A survey;Khan;IEEE Commun. Surv. Tutor.,2012
5. Activity classification with smart phones for sports activities;Taylor;Procedia Eng.,2011