Activity recognition via correlation coefficients based graph with nodes updated by multi-aggregator approach. (January 2023)

• Record Type: Journal Article
• Authors: Hu, Lingyue; Zhao, Kailong; Ling, Bingo Wing-Kuen; Lin, Yuxin
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: Activity recognition -- Structured data -- Graph theory -- Updating features -- Multi-aggregator; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104255
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper proposes a graph theory approach to perform the human activity recognition. This paper proposes the correlation coefficient based method for generating the graph using the signals in the UCI-HAR dataset. The predefined thresholds are used for determining whether the nodes are connected or not. The features are updated according to the activities via the multi-aggregation fusion approach. The random forest is used to classify these activities. Abstract: This paper proposes a graph theory approach to perform the human activity recognition. However, as the most common signal employed for performing the activity recognitions is the motion signal while the motion signal is well structured, ordered and independent one another, the graph theory cannot be applied directly. To address this issue, this paper proposes the correlation coefficient based method for generating the graph using the signals in the UCI-HAR dataset. Here, the predefined thresholds are used for determining whether the nodes are connected or not. The features are updated according to the activities via the multi-aggregation fusion approach. Finally, the random forest is used to classify these activities. To demonstrate the effectiveness of our proposed method, the percentage accuracy and the macro averaged F1 score yielded by our proposed method with the graph weights are compared to those without the graph weights as well as with the multi-aggregator are compared with the mean aggregator. Also, our proposed method is compared to some common methods such as those based on the CNN and SVM. It is found that our proposed method can achieve the percentage accuracy up to 98.74%, which significantly outperforms the existing methods.