Trajectory formation based on physiological characteristics of skeletal muscles

Kashima, Tadashi; Isurugi, Yoshihisa

doi:10.1007/s004220050445

Cited by 32 publications

(19 citation statements)

References 24 publications

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“…Several theories of motor control are based on or inspired by the idea that the CNS arrives at a set of muscle activation patterns for a given task by minimizing a cost function in which minimization of energy cost takes part (e.g., Alexander 1997;Cruse 1986;Engelbrecht 2001;Hatze and Buys 1977;Kashima and Isurugi 1998;Nelson 1983;Rasmussen et al 2001;Soechting et al 1995;Todorov 2004;Todorov and Jordan 2002;Torres and Zipser 2002). Such theories have been shown to predict kinematic features for movement tasks in the absence of external loads, like relatively straight hand paths and bell-shaped velocity profiles.…”

Section: Introductionmentioning

confidence: 99%

The Central Nervous System Does Not Minimize Energy Cost in Arm Movements

Kistemaker

Wong

Gribble

2010

Journal of Neurophysiology

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. It has been widely suggested that the many degrees of freedom of the musculoskeletal system may be exploited by the CNS to minimize energy cost. We tested this idea by having subjects making point-to-point movements while grasping a robotic manipulandum. The robot created a force field chosen such that the minimal energy hand path for reaching movements differed substantially from those observed in a null field. The results show that after extended exposure to the force field, subjects continued to move exactly as they did in the null field and thus used substantially more energy than needed. Even after practicing to move along the minimal energy path, subjects did not adapt their freely chosen hand paths to reduce energy expenditure. The results of this study indicate that for point-to-point arm movements minimization of energy cost is not a dominant factor that influences how the CNS arrives at kinematics and associated muscle activation patterns. I N T R O D U C T I O NA fundamental question in motor control is how the CNS coordinates the many kinematic and mechanical degrees of freedom of the body to control posture and movement (Bernstein 1967). Several theories of motor control are based on or inspired by the idea that the CNS arrives at a set of muscle activation patterns for a given task by minimizing a cost function in which minimization of energy cost takes part (e.g., Alexander 1997;Cruse 1986;Engelbrecht 2001;Hatze and Buys 1977;Kashima and Isurugi 1998;Nelson 1983;Rasmussen et al. 2001;Soechting et al. 1995;Todorov 2004;Todorov and Jordan 2002;Torres and Zipser 2002). Such theories have been shown to predict kinematic features for movement tasks in the absence of external loads, like relatively straight hand paths and bell-shaped velocity profiles. However, at least for unperturbed arm movements, many motor control theories predict these same kinematics (e.g., d 'Avella et al. 2006; Flash and Hogan 1985;Uno et al. 1989). In addition, even if kinematics and muscle activation patterns are in accordance with energy minimization, it would not represent evidence that energy cost is minimized by the CNS to generate motor commands. For example, the motor commands that minimize energy cost could have been selected over an evolutionary timescale.The goal of the present study was to directly test the extent to which energy cost is minimized by the CNS in the context of a motor learning task. One of the most well studied motor learning tasks involves making arm movements while grasping a robotic device that applies forces to the hand that are perpendicular and proportional to the hand velocity (Caithness et al. 2004). Unfortunately, the mechanics of these so-called curl fields are such that the minimal energy path is similar to movements in the absence of forces (see METHODS). Therefore this motor learning task is unsuitable to investigate whether energy minimization plays a role in the selection of kinematics and/or muscle activation patterns by the CNS.In the present study we designed a novel force field ...

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Section: Introductionmentioning

confidence: 99%

The Central Nervous System Does Not Minimize Energy Cost in Arm Movements

Kistemaker

Wong

Gribble

2010

Journal of Neurophysiology

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“…Many scholars have made great efforts to explain observed trajectories as solutions to optimization problems. Optimization criteria have been proposed including minimum jerk theory (Flash and Hogan, 1985), minimum travel cost theory (Rosenbaum et al, 1995), minimum isometric torque derivative (Kashima and Isurugi, 1998), and averaged specific power (Secco et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Energy Optimal Trajectories in Human Arm Motion Aiming for Assistive Robots

Zhou¹,

Bai²,

Li³

2017

MIC

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The energy expenditure in human arm has been of great interests for seeking optimal human arm trajectories. This paper presents a new way for calculating metabolic energy consumption of human arm motions. The purpose is to reveal the relationship between the energy consumption and the trajectory of arm motion, and further, the acceleration and arm orientation contributions. Human arm motion in horizontal plane is investigated by virtue of Qualisys motion capture system. The motion data is post-processed by a biomechanical model to obtain the metabolic expenditure. Results on the arm motion kinematics, dynamics and metabolic energy consumption, are included.

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“…Humanlike body motions are necessary to implement humanlike behavior in an android. There have been several studies on the generation of humanlike motion, including studies on a model to generate human motion trajectories based on a neurocomputational approach (Flash & Hogan, 1985;Uno et al, 1989;Kawato, 1992;Schaal & Sternad, 2001), a study on the control of a manipulator based on a model of motion trajectories of a person's arm (Kashima & Isurugi, 1998), and studies on a computer graphics (CG) animated characters, which have shown that noise in the motion makes the character's motion more humanlike (Perlin, 1995;Bodenheimer et al, 1999). These studies successfully generated a humanlike motion.…”

Section: Introductionmentioning

confidence: 99%