Robust elbow angle prediction with aging soft sensors via output-level domain adaptation

Zhu, Zhongguan, Guo, Shihui, Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126, Chen, Xiaowei, Wu, Ronghui, Shi, Yating, Liu, Xiangyang and Liao, Minghong 2021. Robust elbow angle prediction with aging soft sensors via output-level domain adaptation. IEEE Sensors Journal 21 (20) , pp. 22976-22984. 10.1109/JSEN.2021.3091004

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Abstract

Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors’ resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has “infinite” target domains due to the non-stop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	ORCA email Wed 15/09/2021 12:08 I confirm that the file on record is the accepted version. Thanks for your time. Best regards, Yipeng
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1530-437X
Date of First Compliant Deposit:	15 September 2021
Date of Acceptance:	20 May 2021
Last Modified:	29 Nov 2024 23:45
URI:	https://orca.cardiff.ac.uk/id/eprint/143716

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