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.

This study aimed at the real-time estimation of the lower-limb joint and torso kinematics during a squat exercise, performed in the sagittal plane, using a single inertial measurement unit placed on the lower back. The human body was modeled with a 3-DOF planar chain. The planar IMU orientation and vertical displacement were estimated using one angular velocity and two acceleration components and a weighted Fourier linear combiner. The ankle, knee, and hip joint angles were thereafter obtained through a novel inverse kinematic module based on the use of a Jacobian pseudoinverse matrix and null-space decoupling. The aforementioned algorithms were validated on a humanoid robot for which the mechanical model used and the measured joint angles virtually exhibited no inaccuracies. Joint angles were estimated with a maximal error of 1.5°. The performance of the proposed analytical and experimental methodology was also assessed by conducting an experiment on human volunteers and by comparing the relevant results with those obtained through the more conventional photogrammetric approach. The joint angles provided by the two methods displayed differences equal to 3 ± 1°. These results, associated with the real-time capability of the method, open the door to future field applications in both rehabilitation and sport.

The present study aimed at devising a data processing procedure that provides an optimal estimation of the 3-D instantaneous orientation of an inertial measurement unit (IMU). This result is usually obtained by fusing the data measured with accelerometers, gyroscopes, and magnetometers. Nevertheless, issues related to compensation of integration errors and high sensitivity of these devices to magnetic disturbances call for different solutions. In this study, a method based on the use of gyroscope data only is presented, which uses a Weighted Fourier Linear Combiner adaptive filter to perform a drift-free estimate of the 3D orientation of an IMU located on the lower trunk during walking.

The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180° turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns.

Introducción: Este documento describe el uso del filtro de Kalman extendido utilizando sensores inerciales (IMU), con el fin de realizar la identificación de los parámetros cinemáticos de la marcha humana a un bajo costo. Objetivo: Evaluar un método eficiente y de bajo costo para identificar los parámetros de la cinemática de la marcha humana. Metodología: Se obtuvo el modelo matemático de los miembros inferiores de un ser humano, al cual se le incluyeron cuatro sensores inerciales (IMU). Se tomaron datos reales que fueron introducidos al modelo con el fin de identificar los parámetros cinemáticos. Se utilizó también un sistema óptico VICON con el fin de comparar los resultados obtenidos a partir del filtro de Kalman extendido. Resultados: Los resultados obtenidos de la estimación de parámetros de la marcha humana con el método del filtro de Kalman Extendido, fueron comparados con el método tradicional utilizando un costoso sistema VICON, obteniéndose resultados similares. Conclusiones: El uso del filtro de Kalman extendido permite identificar de manera fácil los parámetros cinemáticos de la marcha humana para ser utilizados en la evaluación de protocolos de tratamiento.

Introducción: Este documento describe el uso del filtro de Kalman extendido utilizando sensores inerciales (IMU), con el fin de realizar la identificación de los parámetros cinemáticos de la marcha humana a un bajo costo. Objetivo: Evaluar un método eficiente y de bajo costo para identificar los parámetros de la cinemática de la marcha humana. Metodología: Se obtuvo el modelo matemático de los miembros inferiores de un ser humano, al cual se le incluyeron cuatro sensores inerciales (IMU). Se tomaron datos reales que fueron introducidos al modelo con el fin de identificar los parámetros cinemáticos. Se utilizó también un sistema óptico VICON con el fin de comparar los resultados obtenidos a partir del filtro de Kalman extendido. Resultados: Los resultados obtenidos de la estimación de parámetros de la marcha humana con el método del filtro de Kalman Extendido, fueron comparados con el método tradicional utilizando un costoso sistema VICON, obteniéndose resultados similares. Conclusiones: El uso del filtro de Kalman extendido permite identificar de manera fácil los parámetros cinemáticos de la marcha humana para ser utilizados en la evaluación de protocolos de tratamiento.