Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.
A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle st...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 2/3; pp. 431 - 441 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2016
|
| 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=113881187&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113881187 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2016 vid: 54 iid: 2/3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 113881187 113881187 NLM26088358 10.1007/s11517-015-1327-2 NLM26088358 113881187 ppf: 431 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms. aug: au: Ghiasi, Mohammad Arjmand, Navid Boroushaki, Mehrdad Farahmand, Farzam Ghiasi, Mohammad Sadegh affil: Mechanical Engineering Department, Sharif University of Technology, Azadi Avenue Tehran Iran sug: subj: Torso Physiology Lifting Algorithms Muscle, Skeletal Physiology Task Performance and Analysis Models, Biological Spine Physiology ab: A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|