Trajectory Estimation of Human Mass Center Based on an Inertia Identification Approach

Author(s):  
Wenwu Xiu ◽  
Qi Lu ◽  
Ou Ma

Mass center of a human body is not a fixed point on the human body because the inertia distribution of the human body changes with body posture. Real-time estimation of the location of human mass center is often required for many biomechanical or biomedical applications. This is not an easy task if the inertia properties of the human’s body segments are unknown. This paper presents a technique for estimating the trajectory of the human mass center based on a recently developed inertia properties identification technology which was derived based on the impulse-momentum principle. The proposed technique assumes a human body as a general treelike multibody system, such that the mass center of the human is predictable with the knowledge of the barycentric parameters of the human. The latter can be identified using inertia identification method. This technique is advantageous because it requires only the 3D motion capture data as its primary input and does not need to know the inertia and geometric parameters of individual body segments of the human. The paper presents a dynamic simulation based study of the proposed estimation technique and also describes an ongoing experimental testing.

Author(s):  
Shoichiro Iwasawa ◽  
Kazuyuki Ebihara ◽  
Jun Ohya ◽  
Ryohei Nakatsu ◽  
Shigeo Morishima

2009 ◽  
Vol 2 (1) ◽  
pp. 1626-1632 ◽  
Author(s):  
Rajeev Penmatsa ◽  
Rajankumar Bhatt ◽  
Kimberly Farrell ◽  
Brent Rochambeau ◽  
Carl Fruehan ◽  
...  

2020 ◽  
Vol 86 (4) ◽  
pp. 61-65
Author(s):  
M. V. Abramchuk ◽  
R. V. Pechenko ◽  
K. A. Nuzhdin ◽  
V. M. Musalimov

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.


2013 ◽  
Vol 39 (10) ◽  
pp. 1722
Author(s):  
Zhao-Wei SUN ◽  
Wei-Chao ZHONG ◽  
Shi-Jie ZHANG ◽  
Jian ZHANG

2021 ◽  
Vol 602 ◽  
pp. 120624
Author(s):  
Reza Kamyar ◽  
David Lauri Pla ◽  
Anas Husain ◽  
Giuseppe Cogoni ◽  
Zilong Wang

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4068
Author(s):  
Zheshuo Zhang ◽  
Jie Zhang ◽  
Jiawen Dai ◽  
Bangji Zhang ◽  
Hengmin Qi

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.


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