Literature DB >> 15333205

Different predictions by the minimum variance and minimum torque-change models on the skewness of movement velocity profiles.

Hirokazu Tanaka1, Meihua Tai, Ning Qian.   

Abstract

We investigated the differences between two well-known optimization principles for understanding movement planning: the minimum variance (MV) model of Harris and Wolpert (1998) and the minimum torque change (MTC) model of Uno, Kawato, and Suzuki (1989). Both models accurately describe the properties of human reaching movements in ordinary situations (e.g., nearly straight paths and bell-shaped velocity profiles). However, we found that the two models can make very different predictions when external forces are applied or when the movement duration is increased. We considered a second-order linear system for the motor plant that has been used previously to simulate eye movements and single-joint arm movements and were able to derive analytical solutions based on the MV and MTC assumptions. With the linear plant, the MTC model predicts that the movement velocity profile should always be symmetrical, independent of the external forces and movement duration. In contrast, the MV model strongly depends on the movement duration and the system's degree of stability; the latter in turn depends on the total forces. The MV model thus predicts a skewed velocity profile under many circumstances. For example, it predicts that the peak location should be skewed toward the end of the movement when the movement duration is increased in the absence of any elastic force. It also predicts that with appropriate viscous and elastic forces applied to increase system stability, the velocity profile should be skewed toward the beginning of the movement. The velocity profiles predicted by the MV model can even show oscillations when the plant becomes highly oscillatory. Our analytical and simulation results suggest specific experiments for testing the validity of the two models.

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Year:  2004        PMID: 15333205     DOI: 10.1162/0899766041732431

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  3 in total

1.  Direction-dependent arm kinematics reveal optimal integration of gravity cues.

Authors:  Jeremie Gaveau; Bastien Berret; Dora E Angelaki; Charalambos Papaxanthis
Journal:  Elife       Date:  2016-11-02       Impact factor: 8.140

2.  High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff.

Authors:  Mazen Al Borno; Saurabh Vyas; Krishna V Shenoy; Scott L Delp
Journal:  Elife       Date:  2020-12-16       Impact factor: 8.140

3.  Biologically inspired modelling for the control of upper limb movements: from concept studies to future applications.

Authors:  Silvia Conforto; Ivan Bernabucci; Giacomo Severini; Maurizio Schmid; Tommaso D'Alessio
Journal:  Front Neurorobot       Date:  2009-11-17       Impact factor: 2.650

  3 in total

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