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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Making rapid and proper compensatory postural adjustments is vital to prevent falls and fall-related injuries. This study aimed to investigate how, especially how rapidly, the multiple lower-limb muscles and joints would respond to the unexpected standing balance perturbations. Unexpected waist-pull perturbations with small, medium and large magnitudes were delivered to twelve healthy young adults from the anterior, posterior, medial and lateral directions. Electromyographical (EMG) and mechanomyographical (MMG) responses of eight dominant-leg muscles (i.e., hip abductor/adductors, hip flexor/extensor, knee flexor/extensor, and ankle dorsiflexor/plantarflexors) together with the lower-limb joint angle, moment, and power data were recorded. The onset latencies, time to peak, peak values, and/or rate of change of these signals were analyzed. Statistical analysis revealed that: (1) agonist muscles resisting the delivered perturbation had faster activation than the antagonist muscles; (2) ankle muscles showed the largest rate of activation among eight muscles following both anteroposterior and mediolateral perturbations; (3) lower-limb joint moments that complied with the perturbation had faster increase; and (4) larger perturbation magnitude tended to evoke a faster response in muscle activities, but not necessarily in joint kinetics/kinematics. These findings provided insights regarding the underlying mechanism and lower-limb muscle activities to maintain reactive standing balance in healthy young adults.

Details

Title
How Does Lower Limb Respond to Unexpected Balance Perturbations? New Insights from Synchronized Human Kinetics, Kinematics, Muscle Electromyography (EMG) and Mechanomyography (MMG) Data
Author
Ringo Tang-Long Zhu 1   VIAFID ORCID Logo  ; Pei-Zhao Lyu 2 ; Li, Shuai 1   VIAFID ORCID Logo  ; Tong, Cheuk Ying 2 ; Yan To Ling 2   VIAFID ORCID Logo  ; Zong-Hao, Christina, Ma 1   VIAFID ORCID Logo 

 Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; [email protected] (R.T.-L.Z.); [email protected] (P.-Z.L.); [email protected] (S.L.); [email protected] (C.Y.T.); [email protected] (Y.T.L.); Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China 
 Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; [email protected] (R.T.-L.Z.); [email protected] (P.-Z.L.); [email protected] (S.L.); [email protected] (C.Y.T.); [email protected] (Y.T.L.) 
First page
430
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20796374
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2679658510
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.