Evidence map›Paper›PMID 40097881›Full record

ArticleAnnals of biomedical engineering2025

Controlling Negative and Positive Power for Efficiency Enhancement and Muscle Strain Mitigation During Squatting with a Portable Knee Exoskeleton.

Shuangyue Yu, Lu Liu, Sainan Zhang, Antonio Di Lallo, Junxi Zhu, Qifei Wu, Guoyu Zuo, Xianlian Zhou, Hao Su

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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Shuangyue YuFaculty of Information Technology and the Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing University of Technology, Beijing, 100124, China.
Lu LiuFaculty of Information Technology and the Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing University of Technology, Beijing, 100124, China.
Sainan ZhangLab of Biomechatronics and Intelligent Robotics, Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA.
Antonio Di LalloLab of Biomechatronics and Intelligent Robotics, Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA.
Junxi ZhuLab of Biomechatronics and Intelligent Robotics, Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA.
Qifei WuFaculty of Information Technology and the Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing University of Technology, Beijing, 100124, China.
Guoyu ZuoFaculty of Information Technology and the Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing University of Technology, Beijing, 100124, China. zuoguoyu@bjut.edu.cn.
Xianlian ZhouDepartment of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
Hao SuLab of Biomechatronics and Intelligent Robotics, Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA. hao.su@nyu.edu.ORCID http://orcid.org/0000-0003-3299-7418

Funding

CDC HHS 75D30120P08812National Coordination Office CAREER award CMMI 1944655National Institute for Occupational Safety & Health 75D30123P17823National Institute on Disability, Independent Living, and Rehabilitation Research SFGE22000372National Natural Science Foundation of China 62403020National Nature Science Foundation of China 62373016National Science Foundation 2344956National Science Foundation Future of Work 2026622Open Projects Program of State Key Laboratory of Multimodal Artificial Intelligence Systems MAIS-2023-22
6 · The paper itself

Abstract

purposeWorkers face a notable risk of musculoskeletal injuries when performing squatting tasks. Knee exoskeletons offer a promising solution to mitigate muscle strain through squat assistance. However, existing studies on knee exoskeletons lack a comprehensive study that meets the multifaceted requirements of squatting assistance in terms of portability, efficiency, and muscle strain mitigation. Furthermore, another open research question pertains to the control strategy of squat assistance, which should be adaptable to various postures and cadences for different individuals. In particular, the effect of controlling negative power assistance during the squat-down phase is not studied.

methodsTo fill these two gaps, first, we develop a simple (computationally efficient and implementable in a microcontroller) and generalizable (for different postures, cadences, and individuals) torque controller for portable knee exoskeletons that delivers both negative and positive power. Our portable knee exoskeleton can benefit users by enhancing efficiency (reducing metabolic cost, heart rate, breathing ventilation), mitigating muscle strain (reducing EMG), and reducing perceived exertion (reducing Borg 6-20 scale) during squatting. Second, we study the effect of three levels of negative power assistance during the squat-down phase.

resultsThis study integrates comprehensive biomechanics and physiology analyses that evaluate our exoskeleton's effectiveness using four objective and two subjective metrics with a group of able-bodied subjects (n = 7). The exoskeleton reduced metabolic cost by 12.8%, heart rate by 13.8%, breathing ventilation by 8.9%, and reduced extensor muscle activity by 39.4-43.2%, flexor muscle activity by 18.9-20.3%, and Borg perceived exertion rate by 1.8 during squatting compare with not wearing the robot.

conclusionDifferent from the musculoskeletal model predictions that suggest increasing benefit with a higher level of negative power assistance, we find that the best performances were achieved with a moderate level of negative power assistance, followed by no assistance and then high assistance.

Indexed as

Exoskeleton DeviceKneeKnee JointMuscle, SkeletalPostureAdultFemaleHumansMaleYoung AdultDynamic modelNegative powerPortable knee exoskeletonSquattingWearable robotics

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.