Center of mass estimation using SESC – Kinect
An increasingly aging society creates the need for a reliable evaluation of postural stability, specially for rehabilitation. Estimation of a subject’s center of mass (CoM) is important for the assessment of unsupported, stable standing. A portable, in-home estimation of CoM can be used as a rehabilitation tool and could be achieved using a Microsoft’s Kinect with statically equivalent serial chains (SESC). This framework makes it feasible to perform subjectspecific center of mass estimation in the home environment.
SESC online identification with a constrained Kalman filter
To estimate the subject-specific 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.
Adaptive Interface for Personalized Center of Mass Self-Identification in Home Rehabilitation
As the center of mass (CoM) position can be used to determine stability, current rehabilitation standards may be improved by tracking it. A personalized CoM estimate can be obtained using the SESC once the model parameters are identified. The identification phase can be completed in a patient’s home using sensors like the Kinect and the Wii balance board.
This work focuses on: improving the SESC identification quality and speed, and using the estimated CoM to determine stability.
Identification time is reduced by creating a visual adaptive interface where the subject’s limbs are colore based on the convergence of the SESC parameters.
The interface that was developed can be used by a subject to track his/her CoM position in a self-directed way. Stability can then be determined using a dynamic index obtained from the estimated CoM trajectory using only Kinect measurements.
IMU motion capture
The movement of the arm reconstructed using the IMU data from a smartphone. The arm is modeled as a 5 dof manipulator and follows the orientation of a sensor attached to the wrist.