Online identification and visualization of the statically equivalent serial chain via constrained Kalman filter

Abstract

A human's center of mass (CoM) trajectory is useful to evaluate the dynamic stability during daily life activities such as walking and standing up. To estimate the subjectspecific CoM position in the home environment, we make use of a statically equivalent serial chain (SESC) developed with a portable measurement system. In this paper we implement a constrained Kalman filter to achieve an online estimation of the SESC parameters while accounting for the human body's bilateral symmetry. This results in constraining SESC parameters to be consistent with the human skeletal model used. The proposed identification method can inform the subject or the therapist, in real-time, about the quality of the on-going CoM estimation. This information can be helpful to reduce the identification time and establish a personalized protocol. A Kinect is used as a markerless motion capture system for measuring limb orientations while the Wii board is used to measure the subject's center of pressure (CoP) during the identification phase. CoP measurements and Kinect data were recorded for four able-bodied subjects. The recorded data was then given to the proposed recursive algorithm to identify the parameters of the SESC online. A cross-validation test was performed to verify the identification performance. The results for these subjects are shown and discussed.

Center of Mass Estimation for Rehabilitation in a Multi-contact Environment: A Simulation Study

“…After enough measurements have been performed, R can be estimated in the least-squares sense using the MoorePenrose pseudo inverse [6], [12]. A suitable estimate can also be obtained on-line by a recursive approach such as the Kalman Filter [9]. Details for the explicit calculation and estimation of R can be found in [6], [12].…”

“…Previous studies [6], [8], [9], [12] have used a force platform to measure CoP position for a number of static poses. This provides two of the three needed components for estimating R of a threedimensional SESC.…”

“…This prevents the application of the SESC on individuals suffering from a balance impairment or a having a high fall risk. With this in mind, we have studied the SESC when calibrated in a on-line manner [9], seeking to reduce the physical demands of the identification procedure. It is also possible to offer real-time feedback to the patient to decrease calibration time.…”

“…That is, our goal is to obtain a subject-specific CoM estimate for patients who require multiple supporting contact points during standing or walking. As in [9], a constrained linear Kalman filter is used. Both data selection, and parameters estimation are done during run-time.…”

Human gait capture by tracking joint motion based on Kalman Filter

“…This fact underlines the importance of developing technologies to support the rehabilitation environment of elderly individuals [1]. In the process of the traditional lower limb rehabilitation medical, commonly used way is a physical therapist artificial rehabilitation.…”