Identification of COM Control Behavior of a Human in Stance as a Dynamical System

Murai, Nobuyuki; Sugihara, Tomomichi

doi:10.1109/biorob49111.2020.9224431

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…In the stepping and braking motion operation, control is performed while predicting the state of a finite time ahead even in the transitional period of the support area change after landing, so if the control input does not change sharply, it is possible to formulate by model prediction control. Sugihara et al proposed model predictive control in the foot control of humanoid robots ( Sugihara and Nakamura, 2006 ; Murai and Sugihara, 2020 ).…”

Section: Previous Research On Identification Of Standing Stability Co...mentioning

confidence: 99%

Identification of Human Walking Balance Controller Based on COM-ZMP Model of Humanoid Robot

Yoshikawa

2022

Front. Robot. AI

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The purpose of this research is to build a technology that enables wearable robotic systems that support human movement to maintain stable balance. By expanding our knowledge of conventional human gait analysis technology and robotics technology, we will build a technology that can estimate the state of human balance. In order to build a technology for estimating the human balance state based on the balance control technology of humanoid robots, we conducted joint research with Osaka University. We applied our knowledge of humanoid robot control to human stepping and braking motions, and confirmed the effectiveness of the balance control model using data measured by a motion capture system and a floor reaction force sensor system. In order to build a technology for estimating the human balance state based on the balance control technology of humanoid robots, we conducted joint research with Osaka University. We applied our knowledge of humanoid robot control to human stepping and braking motions to build a human balance control model. We confirmed the effectiveness of the balance control model using data measured by a motion capture system and a floor reaction force sensor system. In order to understand the state of human walking, the human walking motion was measured by motion capture and analyzed in detail. Following the norms of gait analysis techniques, we extended the balance control model of human foot-stepping and braking motions to a gait model that includes continuous straight-line walking and change of direction during walking. The effectiveness of the constructed balance control model was confirmed using a motion capture system and a floor reaction force sensor system.

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Section: Previous Research On Identification Of Standing Stability Co...mentioning

confidence: 99%

Identification of Human Walking Balance Controller Based on COM-ZMP Model of Humanoid Robot

Yoshikawa

2022

Front. Robot. AI

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“…Earlier versions of this work were presented at 2012 IEEE-RAS International Conference on Humanoid Robots ( Kaneta et al, 2012 ), 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems ( Kaneta et al, 2013 ; Murai et al, 2013 ), and The 8th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics ( Murai and Sugihara, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

Sugihara

Kaneta²,

Murai³

2022

Front. Robot. AI

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This article proposes a process to identify the standing stabilizer, namely, the controller in humans to keep upright posture stable against perturbations. We model the controller as a piecewise-linear feedback system, where the state of the center of mass (COM) is regulated by coordinating the whole body so as to locate the zero-moment point (ZMP) at the desired position. This was developed for humanoid robots and is possibly able to elaborate the fundamental control scheme used by humans to stabilize themselves. Difficulties lie on how to collect motion trajectories in a wide area of the state space for reliable identification and how to identify the piecewise-affine dynamical system. For the former problem, a motion measurement protocol is devised based on the theoretical phase portrait of the system. Regarding the latter problem, some clustering techniques including K-means method and EM (Expectation-and-Maximization) algorithm were examined. We found that a modified K-means method produced the most accurate result in this study. The method was applied to the identification of a lateral standing controller of a human subject. The result of the identification quantitatively supported a hypothesis that the COM-ZMP regulator reasonably models the human’s controller when deviations of the angular momentum about the COM are limited.

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Identification of a Step-and-Brake Controller of a Human Based on COM-ZMP Model and Terminal Capturability Condition

Kojima

Sugihara

2020

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

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Identification of COM Control Behavior of a Human in Stance as a Dynamical System

Cited by 4 publications

References 27 publications

Identification of Human Walking Balance Controller Based on COM-ZMP Model of Humanoid Robot

Identification of Human Walking Balance Controller Based on COM-ZMP Model of Humanoid Robot

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

Identification of a Step-and-Brake Controller of a Human Based on COM-ZMP Model and Terminal Capturability Condition

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