Estimation of body segment inertia parameters from 3D body scanner images: a semi-automatic method dedicated to human movement analysis applications

Robert, Thomas; Leborgne, P.; Beurier, Georges; Dumas, Raphaël

doi:10.1080/10255842.2017.1382920

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…As for a possible alternative to taking photographs, body segmental inertial parameters and positions of anatomical landmarks and MIMUs could be retrieved from body meshes obtained with a 3D scanner. A semi-automatic method, requiring less than one minute of acquisition, has been proposed and validated in nine healthy participants [ 59 ]. Its validity for impaired people, in particular for people with a lower-limb prosthesis, remains to be verified.…”

Section: Discussionmentioning

confidence: 99%

Estimation of 3D Body Center of Mass Acceleration and Instantaneous Velocity from a Wearable Inertial Sensor Network in Transfemoral Amputee Gait: A Case Study

Simonetti

Bergamini

Vannozzi

et al. 2021

Sensors

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The analysis of the body center of mass (BCoM) 3D kinematics provides insights on crucial aspects of locomotion, especially in populations with gait impairment such as people with amputation. In this paper, a wearable framework based on the use of different magneto-inertial measurement unit (MIMU) networks is proposed to obtain both BCoM acceleration and velocity. The proposed framework was validated as a proof of concept in one transfemoral amputee against data from force plates (acceleration) and an optoelectronic system (acceleration and velocity). The impact in terms of estimation accuracy when using a sensor network rather than a single MIMU at trunk level was also investigated. The estimated velocity and acceleration reached a strong agreement (ρ > 0.89) and good accuracy compared to reference data (normalized root mean square error (NRMSE) < 13.7%) in the anteroposterior and vertical directions when using three MIMUs on the trunk and both shanks and in all three directions when adding MIMUs on both thighs (ρ > 0.89, NRMSE ≤ 14.0% in the mediolateral direction). Conversely, only the vertical component of the BCoM kinematics was accurately captured when considering a single MIMU. These results suggest that inertial sensor networks may represent a valid alternative to laboratory-based instruments for 3D BCoM kinematics quantification in lower-limb amputees.

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Section: Discussionmentioning

confidence: 99%

Estimation of 3D Body Center of Mass Acceleration and Instantaneous Velocity from a Wearable Inertial Sensor Network in Transfemoral Amputee Gait: A Case Study

Simonetti

Bergamini

Vannozzi

et al. 2021

Sensors

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“…X, Y is a coordinate system with the center of the robot as the origin, and the positive direction of the robot is the coordinate system of the OX axis. e angle between the robot and OX is represented by θ [10].…”

Section: Football Robot Action Algorithmmentioning

confidence: 99%

Real-Time Evaluation Algorithm of Human Body Movement in Football Training Robot

Lin²

2021

Mathematical Problems in Engineering

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As one of the most challenging topics in the field of artificial intelligence, soccer robots are currently an important platform for humanoid robotics research. Its fields cover a wide range of fields, including robotics, artificial intelligence, and automatic control. Kinematics analysis and action planning are the key technologies in the research of humanoid soccer robots and are the basis for realizing basic actions such as walking. This article mainly introduces the real-time evaluation algorithm of human motion in the football training robot. The football robot action evaluation algorithm proposed here designs the angle and wheel speed of the football robot movement through the evaluation of the angular velocity and linear velocity of the center of mass of the robot. The overall system of the imitation human football robot is studied, including the mechanical system design. The design of the leg structure, the decision-making system based on the finite state machine, the robot vision system, and the image segmentation technology are introduced. The experimental results in this article show that the action of the football training robot model is very stable, the static rotation movement time is about 220 ms, and the fixed-point movement error is less than 1 cm, which fully meets the accuracy requirements of the large-space football robot.

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“…to conduct analyses of STA [29,30]. It is also possible to analyze the shape of body segments, to estimate body segment inertial parameters [31], and exploit other topological data. The benefit of those advantages, besides the fact of not having to instrument the users, can compensate current drawbacks of 3DTS technology, such as the higher cost of equipment and having to manage larger data sets, or limitations in the kind of scenes that can be tracked; e.g.…”

Section: Tablementioning

confidence: 99%

Accuracy of a 3D Temporal Scanning System for Gait Analysis: Comparative with a Marker-Based Photogrammetry System

et al. 2022

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Estimation of body segment inertia parameters from 3D body scanner images: a semi-automatic method dedicated to human movement analysis applications

Cited by 5 publications

References 8 publications

Estimation of 3D Body Center of Mass Acceleration and Instantaneous Velocity from a Wearable Inertial Sensor Network in Transfemoral Amputee Gait: A Case Study

Estimation of 3D Body Center of Mass Acceleration and Instantaneous Velocity from a Wearable Inertial Sensor Network in Transfemoral Amputee Gait: A Case Study

Real-Time Evaluation Algorithm of Human Body Movement in Football Training Robot

Accuracy of a 3D Temporal Scanning System for Gait Analysis: Comparative with a Marker-Based Photogrammetry System

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