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Guidelines of the Target Muscle Activation Protocol during Trunk Rehabilitation Robot-based Seated Perturbation Training

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Abstract
Rehabilitation interventions involving forced perturbations of seated balance can improve a patient’s balancing ability with increased muscle activations. Investigating training parameters that affect the specific muscle activation is essential to providing patient-tailored training. However, there is limited information on the combined effects of different seated perturbation training parameters to provide target activation of lower limb muscles although various parameters can affect activating lower limb muscles. Accordingly, by using the trunk rehabilitation robot that can provide quantified perturbation, we investigated the effects of variations in perturbation speed (slow (SS) and fast (FS) speed), direction (mediolateral (ML)& anteroposterior (AP)), and leg support condition (seat-connected (SC) and ground-connected (GC) footrest) on the lower limb muscle activity as well as postural stability and trunk movement of 18 healthy young participants. The experimental results show that variation in training parameters affects balance, trunk movement, and muscle activity outcomes. From the experimental results, FS & GC with AP-directional perturbation can be the effective training protocol for Gastrocnemius (0.16 %PA, percentage of Peak Amplitude). In addition, considering balance instability results, FS & SC with AP-directional perturbation can be adopted as the effective training protocol to target the Vastus Lateralis (0.15 %PA), Semitendinosus (0.12 %PA), and Tibialis Anterior (0.16 %PA). Thus, modulation of footrest condition, perturbation speed and direction may be feasible for tuning perturbation exercises to achieve desired rehabilitation outcomes. © 2013 IEEE.
Author(s)
Eizad, AmreLee, HosuLee, JunyeongSong, Won-KyungYoon, Jungwon
Issued Date
2025-04
Type
Article
DOI
10.1109/ACCESS.2025.3559920
URI
https://scholar.gist.ac.kr/handle/local/18744
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Access, v.13, pp.78150 - 78160
ISSN
2169-3536
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
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