Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control

Abstract Human movement modeling has been the object of much research for the past 30 years. In these models the position of foot link was fixed on the ground. We propose to model the feet links as variable, since the position of foot pressure center changes from heel to toes. The ground reaction forces could also be analyzed in real time. We examined this model for some static postures. In standing anatomical position, the maximum articular forces are localized in hip and knee joints. In sagittal plane, the ground reaction force vectors are positioned nearly under ankle joints. The pathological postures like body with pes cavus or with global spine kyphosis increase the articular and muscular forces. In these cases, the position of ground reaction force vectors is moved toward the toes.

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Prototype-Based Methods for Human Movement Modeling

10.1007/978-3-030-63416-2_378 ◽

2021 ◽

pp. 1030-1033

Author(s):

Zhe Lin ◽

Zhuolin Jiang ◽

Larry S. Davis

Keyword(s):

Human Movement ◽

Human Movement Modeling

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Risk-Aware Control

Neural Computation ◽

10.1162/neco_a_00662 ◽

2014 ◽

Vol 26 (12) ◽

pp. 2669-2691 ◽

Cited By ~ 13

Author(s):

Terence D. Sanger

Keyword(s):

Feedback Control ◽

Robot Control ◽

Human Movement ◽

Spiking Neurons ◽

Cost Functions ◽

Control Law ◽

Time Varying ◽

Unknown Parameters ◽

Current State ◽

The Cost

Human movement differs from robot control because of its flexibility in unknown environments, robustness to perturbation, and tolerance of unknown parameters and unpredictable variability. We propose a new theory, risk-aware control, in which movement is governed by estimates of risk based on uncertainty about the current state and knowledge of the cost of errors. We demonstrate the existence of a feedback control law that implements risk-aware control and show that this control law can be directly implemented by populations of spiking neurons. Simulated examples of risk-aware control for time-varying cost functions as well as learning of unknown dynamics in a stochastic risky environment are provided.

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The Center for Human Modeling and Simulation

Presence Teleoperators & Virtual Environments ◽

10.1162/pres.1995.4.1.81 ◽

1995 ◽

Vol 4 (1) ◽

pp. 81-96 ◽

Cited By ~ 5

Author(s):

Norman I. Badler ◽

Dimitri Metaxas ◽

Bonnie Webber ◽

Mark Steedman

Keyword(s):

Modeling And Simulation ◽

Dynamic Models ◽

Human Movement ◽

Image Synthesis ◽

Human Modeling ◽

Human Movement Modeling ◽

Illumination Models ◽

The Relationship ◽

Rendering Techniques ◽

Human Modeling And Simulation

The overall goals of the Center for Human Modeling and Simulation are the investigation of computer graphics modeling, animation, and rendering techniques. Major focii are in behavior-based animation of human movement, modeling through physics-based techniques, applications of control theory techniques to dynamic models, illumination models for image synthesis, and understanding the relationship between human movement, natural language, and communication.

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Computerized Simulation Of Whole Body Dynamics: Aspects Of Human Movement Modeling

10.1117/12.932316 ◽

1982 ◽

Author(s):

Ronald L. Huston ◽

Ronald F. Zernicke

Keyword(s):

Human Movement ◽

Whole Body ◽

Body Dynamics ◽

Computerized Simulation ◽

Human Movement Modeling

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Probabilistic indoor human movement modeling to aid first responders

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-012-0112-4 ◽

2012 ◽

Vol 4 (5) ◽

pp. 559-569 ◽

Cited By ~ 5

Author(s):

Eoghan Furey ◽

Kevin Curran ◽

Paul Mc Kevitt

Keyword(s):

First Responders ◽

Human Movement ◽

Human Movement Modeling

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Human-inspired motion model of upper-limb with fast response and learning ability – a promising direction for robot system and control

Assembly Automation ◽

10.1108/aa-11-2015-099 ◽

2016 ◽

Vol 36 (1) ◽

pp. 97-107 ◽

Cited By ~ 20

Author(s):

Hong Qiao ◽

Chuan Li ◽

Peijie Yin ◽

Wei Wu ◽

Zhi-Yong Liu

Keyword(s):

Robot Control ◽

Human Movement ◽

Fast Response ◽

Neural System ◽

Human Motion ◽

Learning Ability ◽

Propagation Mechanism ◽

Motion Model ◽

Content Type ◽

Movement System

Purpose – Human movement system is a Multi-DOF, redundant, complex and nonlinear system formed by coordinating combination of neural system, bones, muscles and joints, which is robust and has fast response and learning ability. Imitating human movement system can improve robustness, fast response and learning ability of the robots. Design/methodology/approach – In this paper, we propose a new motion model based on the human motion pathway, especially the information propagation mechanism between the cerebellum and spinal cord. Findings – The proposed motion model proves to have fast response and learning ability through experiments, which matches the features of human motion. Originality/value – The proposed model in this paper introduces the habitual theory in kinesiology and neuroscience into robot control, and improves robustness, fast response and learning ability of the robots. This paper proves that introduction of neuroscience has an important guiding significance for precise and adaptive robot control, such as assembly automation.

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