Imitation Control for Biped Robot Using Wearable Motion Sensor

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Tao Liu ◽  
Yoshio Inoue ◽  
Kyoko Shibata
Keyword(s):  
Motion Analysis ◽  
Control Method ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Biped Robot ◽  
Human Motion ◽  
Optical Tracking ◽  
Human Motion Analysis ◽  
Leg Length ◽  
Using Data

In conventional imitation control, optical tracking devices have been widely adopted to capture human motion and control robots in a laboratory environment. Wearable sensors are attracting extensive interest in the development of a lower-cost human-robot control system without constraints from stationary motion analysis devices. We propose an ambulatory human motion analysis system based on small inertial sensors to measure body segment orientations in real time. A new imitation control method was developed and applied to a biped robot using data of human joint angles obtained from a wearable sensor system. An experimental study was carried out to verify the method of synchronous imitation control for a biped robot. By comparing the results obtained from direct imitation control with an improved method based on a training algorithm, which includes a personal motion pattern, we found that the accuracy of imitation control was markedly improved and the tri-axial average errors of x-y- and z-moving displacements related to leg length were 12%, 8% and 4%, respectively. Experimental results support the feasibility of the proposed control method.

scholarly journals The use of inertial sensors system for human motion analysis

2010 ◽  
Vol 15 (6) ◽  
pp. 462-473 ◽  
Author(s):  
Antonio I Cuesta-Vargas ◽  
Alejandro Galán-Mercant ◽  
Jonathan M Williams
Keyword(s):  
Motion Analysis ◽  
Inertial Sensors ◽  
Human Motion ◽  
Human Motion Analysis

Wearable Inertial Sensors for Human Motion Analysis: A Review

2016 ◽  
Vol 16 (22) ◽  
pp. 7821-7834 ◽  
Author(s):  
Irvin Hussein Lopez-Nava ◽  
Angelica Munoz-Melendez
Keyword(s):  
Motion Analysis ◽  
Inertial Sensors ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Wearable Inertial Sensors

scholarly journals The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

2021 ◽  
Vol 10 ◽  
pp. 117957272110223
Author(s):  
Thomas Hellsten ◽  
Jonny Karlsson ◽  
Muhammed Shamsuzzaman ◽  
Göran Pulkkis
Keyword(s):  
Computer Vision ◽  
Motion Analysis ◽  
Pose Estimation ◽  
Joint Angle ◽  
Human Motion ◽  
Human Pose Estimation ◽  
Human Motion Analysis ◽  
Single View ◽  
Recent Computer ◽  
Human Pose

Background: Several factors, including the aging population and the recent corona pandemic, have increased the need for cost effective, easy-to-use and reliable telerehabilitation services. Computer vision-based marker-less human pose estimation is a promising variant of telerehabilitation and is currently an intensive research topic. It has attracted significant interest for detailed motion analysis, as it does not need arrangement of external fiducials while capturing motion data from images. This is promising for rehabilitation applications, as they enable analysis and supervision of clients’ exercises and reduce clients’ need for visiting physiotherapists in person. However, development of a marker-less motion analysis system with precise accuracy for joint identification, joint angle measurements and advanced motion analysis is an open challenge. Objectives: The main objective of this paper is to provide a critical overview of recent computer vision-based marker-less human pose estimation systems and their applicability for rehabilitation application. An overview of some existing marker-less rehabilitation applications is also provided. Methods: This paper presents a critical review of recent computer vision-based marker-less human pose estimation systems with focus on their provided joint localization accuracy in comparison to physiotherapy requirements and ease of use. The accuracy, in terms of the capability to measure the knee angle, is analysed using simulation. Results: Current pose estimation systems use 2D, 3D, multiple and single view-based techniques. The most promising techniques from a physiotherapy point of view are 3D marker-less pose estimation based on a single view as these can perform advanced motion analysis of the human body while only requiring a single camera and a computing device. Preliminary simulations reveal that some proposed systems already provide a sufficient accuracy for 2D joint angle estimations. Conclusions: Even though test results of different applications for some proposed techniques are promising, more rigour testing is required for validating their accuracy before they can be widely adopted in advanced rehabilitation applications.

Video-Based Human Motion Analysis

2011 ◽  
Vol 403-408 ◽  
pp. 2593-2597
Author(s):  
Hong Bao ◽  
Zhi Min Liu
Keyword(s):  
Motion Analysis ◽  
Human Body ◽  
Body Shape ◽  
Human Motion ◽  
Random Motion ◽  
Human Motion Analysis ◽  
Mass Center ◽  
Shape Features ◽  
Skeleton Model ◽  
Regular Motion

In the analysis of human motion, movement was divided into regular motion (such as walking and running) and random motion (such as falling down).Human skeleton model is used in this paper to do the video-based analysis. Key joints on human body were chosen to be traced instead of tracking the entire human body. Shape features like mass center trajectory were used to describe the movement, and to classify human motion. desired results achieved.

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