An EMG-driven model applied for predicting metabolic energy consumption during movement

被引:11
|
作者
Bisi, Maria Cristina [1 ]
Stagni, Rita [1 ]
Houdijk, Han [2 ]
Gnudi, Gianni [1 ]
机构
[1] Univ Bologna, Dept Elect Comp Sci & Syst, I-47023 Cesena, Italy
[2] Vrije Univ Amsterdam, Fac Human Movement Sci, Res Inst MOVE, Amsterdam, Netherlands
关键词
Musculoskeletal model; Energetic muscle model; EMG-driven model; MUSCLE FORCES; CONTRACTION; EFFICIENCY; TENDON; SYSTEM; KNEE;
D O I
10.1016/j.jelekin.2011.07.003
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The relationship between mechanical work and metabolic energy cost during movement is not yet clear. Many studies demonstrated the utility of forward-dynamic musculoskeletal models combined with experimental data to address such question. The aim of this study was to evaluate the applicability of a muscle energy expenditure model at whole body level, using an EMG-driven approach. Four participants performed a 5-min squat exercise on unilateral leg press at two different frequencies and two load levels. Data collected were kinematics, EMG, forces and moments under the foot and gas-exchange data. This same task was simulated using a musculoskeletal model, which took EMG and kinematics as inputs and gave muscle forces and muscle energetics as outputs. Model parameters were taken from literature, but maximal isometric muscle force was optimized in order to match predicted joint moments with measured ones. Energy rates predicted by the model were compared with energy consumption measured by the gas-exchange data. Model results on metabolic energy consumption were close to the values obtained through indirect calorimetry. At the higher frequency level, the model underestimated measured energy consumption. This underestimation can be explained with an increase in energy consumption of the non-muscular mass with movement velocity. In conclusion, results obtained in comparing model predictions with experimental data were promising. More research is needed to evaluate this way of computing mechanical and metabolic work. (C) 2011 Elsevier Ltd. All rights reserved.
引用
下载
收藏
页码:1074 / 1080
页数:7
相关论文
共 50 条
  • [1] An EMG-driven biomechanical model of the hand
    Buchholz, B
    ADVANCES IN OCCUPATIONAL ERGONOMICS AND SAFETY, VOL 2, 1998, 2 : 443 - 446
  • [2] Predicting Ankle Joint Moments Using A Hybrid EMG-driven Model
    Bassett, Daniel N.
    Manal, Kurt
    Cohen, Shay
    Buchanan, Thomas S.
    MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 2005, 37 : S278 - S278
  • [3] An EMG-driven musculoskeletal model of the shoulder
    Nikooyan, A. A.
    Veeger, H. E. J.
    Westerhoff, P.
    Bolsterlee, B.
    Graichen, F.
    Bergmann, G.
    van der Helm, F. C. T.
    HUMAN MOVEMENT SCIENCE, 2012, 31 (02) : 429 - 447
  • [4] EMG-driven human model for orthosis control
    Fleischer, C
    Hommel, G
    HUMAN INTERACTION WITH MACHINES, 2006, : 69 - +
  • [5] An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement
    Zhao, Yihui
    Li, Zhenhong
    Zhang, Zhiqiang
    Qian, Kun
    Xie, Shengquan
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 81
  • [6] An EMG-driven biomechanical model of the canine cervical spine
    Alizadeh, M.
    Knapik, G. G.
    Dufour, J. S.
    Zindl, C.
    Allen, M. J.
    Bertran, J.
    Fitzpatrick, N.
    Marras, W. S.
    JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2017, 32 : 101 - 109
  • [7] A real-time EMG-driven musculoskeletal model of the ankle
    Manal, Kurt
    Gravare-Silbernagel, Karin
    Buchanan, Thomas S.
    MULTIBODY SYSTEM DYNAMICS, 2012, 28 (1-2) : 169 - 180
  • [8] A real-time EMG-driven musculoskeletal model of the ankle
    Kurt Manal
    Karin Gravare-Silbernagel
    Thomas S. Buchanan
    Multibody System Dynamics, 2012, 28 : 169 - 180
  • [9] Use of an EMG-driven biomechanical model to study virtual injuries
    Manal, K
    Buchanan, TS
    MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 2005, 37 (11): : 1917 - 1923
  • [10] The effectiveness of EMG-driven neuromusculoskeletal model calibration is task dependent
    Kian, Azadeh
    Pizzolato, Claudio
    Halaki, Mark
    Ginn, Karen
    Lloyd, David
    Reed, Darren
    Ackland, David
    JOURNAL OF BIOMECHANICS, 2021, 129