Multi-phase optimisation model predicts manual lifting motions with less reliance on experiment-based posture data.
Optimisation-based predictive models are widely-used to explore the lifting strategies. Existing models incorporated empirical subject-specific posture constraints to improve the prediction accuracy. However, over-reliance on these constraints limits the application of predictive models. This paper...
| Publicado en: | Ergonomics Vol. 66; no. 9; pp. 1398 - 1414 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173414279&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173414279 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: Sep2023 vid: 66 iid: 9 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 173414279 160275345 173414279 173414279 10.1080/00140139.2022.2150322 173414279 ppf: 1398 ppct: 16 formats: tig: atl: Multi-phase optimisation model predicts manual lifting motions with less reliance on experiment-based posture data. aug: au: Zheng, Size Li, Qingguo Liu, Tao affil: State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China sug: subj: Prediction Models Weight Lifting Physiology Posture Physiology Time and Motion Studies Task Performance and Analysis Lifting Methods Squatting Physiology Human Male Female Funding Source Correlation Coefficient Predictive Validity Product Development Assistive Technology Devices Biomechanics Robotics Male Female ab: Optimisation-based predictive models are widely-used to explore the lifting strategies. Existing models incorporated empirical subject-specific posture constraints to improve the prediction accuracy. However, over-reliance on these constraints limits the application of predictive models. This paper proposed a multi-phase optimisation method (MPOM) for two-dimensional sagittally symmetric semi-squat lifting prediction, which decomposes the complete lifting task into three phases—the initial posture, the final posture, and the dynamic lifting phase. The first two phases are predicted with force- and stability-related strategies, and the last phase is predicted with a smoothing-related objective. Box-lifting motions of different box initial heights were collected for validation. The results show that MPOM has better or similar accuracy than the traditional single-phase optimisation (SPOM) of minimum muscular utilisation ratio, and MPOM reduces the reliance on experimental data. MPOM offers the opportunity to improve accuracy at the expense of efforts to determine appropriate weightings in the posture prediction phases. Practitioner summary: Lifting optimisation models are useful to predict and explore the human motion strategies. Existing models rely on empirical subject-specific posture constraints, which limit their applications. A multi-phase model for lifting motion prediction was constructed. This model could accurately predict 2D lifting motions with less reliance on these constraints. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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