This paper presents a new technique for estimating the center of mass of articulated rigid body systems. This estimation technique uses the statically equivalent serial chain, a serial chain representation of any multilink branched chain whose end-effector locates directly the center of mass. This technique works based on a knowledge of only the kinematic architecture of the system and does not require the total mass, or any of the individual body's mass or length properties. This constitutes an advance in center of mass estimation, providing an alternative to techniques requiring continuously a force plate. A comparison of these estimation techniques is presented. The modeling and estimation technique is then implemented on a human subject.

Estimation of the Center of Mass: From Humanoid Robots to Human Beings

Center of mass (CoM) trajectory is important during standing and walking since it can be used as an index for stability and fall prediction. Unfortunately current methods for CoM estimation require the use of specialized equipment (such as motion capture and force platforms) in controlled environments. This paper aims at applying the statically equivalent serial chain (SESC) method to obtain CoM position using widely available and portable hardware; a Microsoft's Kinect and a Nintendo's Wii balance board. During identification, CoM is approximated by CoP measurements and the virtual chain is created for able-bodied subjects. The result demostrates that the SESC method can be applied outside the laboratory environment using a Kinect. Cross-validation of the identified model was performed to evaluate the accuracy of the method. Results obtained of five subjects are shown and discussed.

/pdf/estimation-of-the-center-of-mass-with-kinect-and-wii-balance-38arjc4boe.pdf

Estimation of the center of mass with Kinect and Wii balance board

The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patient's home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution.

/pdf/whole-body-center-of-mass-estimation-with-portable-sensors-xzv7cl5atu.pdf

Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

A posture training paradigm with visual biofeedback is presented for physical rehabilitation following posture disorders (e.g. Pusher Syndrome). The objectives of the current study were: to calculate centre of pressure (CoP) using Wii balance board, to calculate centre of mass (CoM) using Microsoft Kinect sensor, and conceptual design of visual postural-feedback paradigm so that the user can improve functional reach tasks. The algorithm for calculating the posture based on the line connecting CoP-CoM (i.e. the lean-line), which was validated offline with a convenient sampling of 10 elderly (>50 years) subjects, where the maximum angle of the lean-line (with horizontal, i.e. the lean-angle) was compared with the Berg Balance Score - a widely used measure to predict risk of falls in elderly - during maximum forward, sideways, and backward lean. Since both the CoP and CoM moved during the task, the line joining CoP-CoM was indicative of the posture. A high correlation was found between the maximum lean-angle and the Berg Balance Score, and therefore it was proposed as a viable biofeedback for posture training.

Low-cost visual postural feedback with Wii balance board and Microsoft Kinect - a feasibility study

Poststroke hemiplegic patients often show altered weight distribution with balance disorders, increasing their risk of fall. Conventional balance training, though powerful, suffers from scarcity of trained therapists, frequent visits to clinics to get therapy, one-on-one therapy sessions, and monotony of repetitive exercise tasks. Thus, technology-assisted balance rehabilitation can be an alternative solution. Here, we chose virtual reality as a technology-based platform to develop motivating balance tasks. This platform was augmented with off-the-shelf available sensors such as Nintendo Wii balance board and Kinect to estimate one's center of mass (CoM). The virtual reality-based CoM-assisted balance tasks (Virtual CoMBaT) was designed to be adaptive to one's individualized weight-shifting capability quantified through CoM displacement. Participants were asked to interact with Virtual CoMBaT that offered tasks of varying challenge levels while adhering to ankle strategy for weight shifting. To facilitate the patients to use ankle strategy during weight-shifting, we designed a heel lift detection module. A usability study was carried out with 12 hemiplegic patients. Results indicate the potential of our system to contribute to improving one's overall performance in balance-related tasks belonging to different difficulty levels.

/pdf/virtual-reality-based-center-of-mass-assisted-personalized-4o4jvfpsjb.pdf

Virtual Reality-Based Center of Mass-Assisted Personalized Balance Training System.

The control of the center of mass of a robot is a relevant problem in case of biped walking machines. Besides, studying the motion and the stabilization of the center of mass of a human is an important research topic in the area of biomechanics. Finally, the two areas are involved when we want to synthesize certain classes of realistic motions in computer animation. In this paper, we address some of the modelling and control problems which arise when considering the CoM of an articulated chain. In a first part, we show that the position of the CoM of a general tree-structure kinematic chain can always be represented by the end-point position of an equivalent serial open kinematic chain, the geometric parameters of which depend on the mass properties of the original structure. We then use this result in a second part, in which we describe a way of specifying tasks involving the motion of the CoM. We also propose in the paper a general approach of the associated control problem and of its implementation and give an example of application to computer animation.

https://hal.inria.fr/inria-00073210/document

Ronan Boulic

Papers

On the Computation and Control of the Mass Center of Articulated Chains