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Robust Human Activity Recognition Using Lesser Number of Wearable Sensors


Authors: D. Wang, E. Candinegara, J. Hou, A.-H. Tan, and C. Miao
Title: Robust Human Activity Recognition Using Lesser Number of Wearable Sensors
Abstract: In recent years, research on the recognition of human physical activities solely using wearable sensors has received more and more attention. Compared to other types of sensory devices such as surveillance cameras, wearable sensors are preferred in most activity recognition applications mainly due to their non-intrusiveness and pervasiveness. However, many existing activity recognition applications or experiments using wearable sensors were conducted in the confined laboratory settings using specifically developed gadgets. These gadgets may be useful for a small group of people in certain specific scenarios, but probably will not gain their popularity because they introduce additional costs and they are unusual in everyday life. Alternatively, commercial devices such as smart phones and smart watches can be better utilized for robust activity recognitions. However, only few prior studies focused on activity recognitions using multiple commercial devices. In this paper, we present our feature extraction strategy and compare the performance of our feature set against other feature sets using the same classifiers. We conduct various experiments on a subset of a public dataset named PAMAP2. Specifically, we only select two sensors out of the thirteen used in PAMAP2. Experimental results show that our feature extraction strategy performs better than the others. This paper provides the necessary foundation towards robust activity recognition using only the commercial wearable devices.
Keywords: Activity recognition; PAMAP2 dataset; Wearable sensor; Support vector machine; Random forest
Conference Name: International Conference on Security, Pattern Analysis, and Cybernetics (SPAC'17)
Location: Shenzhen, China
Publisher: IEEE
Year: 2017
Accepted PDF File: Robust_Human_Activity_Recognition_Using_Lesser_Number_of_Wearable_Sensors_accepted.pdf
Permanent Link: https://doi.org/10.1109/SPAC.2017.8304292
Reference: D. Wang, E. Candinegara, J. Hou, A.-H. Tan, and C. Miao, “Robust human activity recognition using lesser number of wearable sensors,” in Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC’17). IEEE, December 2017, pp. 290–295.
bibtex: 
@inproceedings{LILY-c141, 
    author	= {Wang, Di and Candinegara, Edwin and Hou, Junhui and Tan, Ah-Hwee and Miao, Chunyan},
    title	= {Robust Human Activity Recognition Using Lesser Number of Wearable Sensors},  
    booktitle	= {Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC'17)}, 
    year		= {2017}, 
    month	= {December}, 
    pages	= {290-295}, 
    location	= {Shenzhen, China},
    publisher	= {IEEE},
 }