Control strategies for trunk exoskeletons based on motion intent recognition: A review

Article type: Review Article

Authors: Yuan, Ruyu^a | Wang, Qingqing^a | Xu, Haipeng^b | Yu, Hongliu^{a; c} | Shi, Ping^{a; c; *}

Affiliations: [a] Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China | [b] Physical Education Department, University of Shanghai for Science and Technology, Shanghai, China | [c] Shanghai Engineering Research Center of Assistive Devices, Shanghai, China

Correspondence: [*] Address for correspondence: Ping Shi, 516 Jungong Road, Shanghai 200093, China. E-mail: [email protected].

Abstract: BACKGROUND:Wearable trunk exoskeletons hold immense potential in fields such as healthcare and industry. Previous research has indicated that intention recognition control plays a crucial role in users’ daily use of exoskeletons. OBJECTIVE:This review aims to discuss the characteristics of intention recognition control schemes for intelligent trunk exoskeletons under different control objectives over the past decade. METHODS:Considering the relatively late development of active trunk exoskeletons, we selected papers published in the last decade (2013 to 2023) from the Web of Science, PubMed, and IEEE Xplore databases. In total, 50 articles were selected and examined based on four control objectives. RESULTS:In general, we found that researchers focus on trunk exoskeleton devices designed for assistance and motor augmentation, which rely more on body movement signals as a source for intention recognition. CONCLUSION:Based on these results, we identify and discuss several promising research directions that may help to attain a widely accepted control methods, thereby advancing further development of trunk exoskeleton technology.

Keywords: Intent recognition, trunk exoskeletons, control strategy, motion of the relevant body parts, biological signals, sensor

DOI: 10.3233/NRE-240066

Journal: NeuroRehabilitation, vol. 54, no. 4, pp. 575-597, 2024