Pedalvatar: An IMU-based real-time body motion capture system using foot rooted kinematic model

2014 ◽  
Author(s):  
Yang Zheng ◽  
Ka-Chun Chan ◽  
Charlie C. L. Wang
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Kinematic Model ◽  
Body Motion ◽  
Motion Capture System

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

scholarly journals A specialized motion capture system for real-time analysis of mandibular movements using infrared cameras

2013 ◽  
Vol 12 (1) ◽  
Author(s):  
Daniel Antônio Furtado ◽  
Adriano Alves Pereira ◽  
Adriano de Oliveira Andrade ◽  
Douglas Peres Bellomo ◽  
Marlete Ribeiro da Silva
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Motion Capture System ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Infrared Cameras ◽  
Mandibular Movements

Natural human–computer interaction control of multi operators in collaborative virtual maintenance based on optical human motion capture system

2016 ◽  
Vol 07 (02) ◽  
pp. 1650002
Author(s):  
Xiangyang Li ◽  
Zhili Zhang ◽  
Feng Liang ◽  
Qinhe Gao ◽  
Lilong Tan
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Motion Capture ◽  
Human Motion ◽  
Motion Capture System ◽  
Human Motion Capture ◽  
Interaction Control ◽  
Time Motion ◽  
Virtual Maintenance ◽  
Computer Interaction

Aiming at the human–computer interaction control (HCIC) requirements of multi operators in collaborative virtual maintenance (CVM), real-time motion capture and simulation drive of multi operators with optical human motion capture system (HMCS) is proposed. The detailed realization process of real-time motion capture and data drive for virtual operators in CVM environment is presented to actualize the natural and online interactive operations. In order to ensure the cooperative and orderly interactions of virtual operators with the input operations of actual operators, collaborative HCIC model is established according to specific planning, allocating and decision-making of different maintenance tasks as well as the human–computer interaction features and collaborative maintenance operation features among multi maintenance trainees in CVM process. Finally, results of the experimental implementation validate the effectiveness and practicability of proposed methods, models, strategies and mechanisms.

scholarly journals Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1076
Author(s):  
Laisi Cai ◽  
Dongwei Liu ◽  
Ye Ma
Keyword(s):  
Motion Analysis ◽  
Motion Capture ◽  
Upper Limb ◽  
Kinematic Model ◽  
Sensor Placement ◽  
Motion Capture System ◽  
Kinect Sensor ◽  
3D Motion ◽  
Flexion Extension ◽  
3D Motion Capture

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system’s performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

Sign in / Sign up

Export Citation Format

Share Document