Vincent Bonnet

This study aims to explore the potential for accurately estimating joint angles during upper limb rehabilitation tasks with different calibration procedures, inverse kinematics methods and measurement modalities. Affordable embedded visual-inertial measurement units offer a promising alternative to the costly and cumbersome gold standard marker-based optical motion capture systems. However, affordability comes with inherent sensors inaccuracies. Hence, prior to their application in a real clinical setting, it is important to demonstrate their ability for accurate joint angle estimation. Discrepancies in joint angles arise due to the inaccuracies of different sensing modalities but also to sensor-to-segment calibration procedures that significantly alter the joint offsets. Therefore, in this paper, the impact of functional and anatomical calibration procedures on joint angle estimation was compared among seven healthy young volunteers. When the same calibration procedures were applied with visual-inertial measurement units and optical motion capture systems data, a relatively small root mean square error of 7.9 deg and correlation coefficients exceeding 0.86 were observed. When different calibration procedures were applied with visual-inertial measurement units and optical motion capture systems data, higher root mean square superior to 10 deg were observed, highlighting the importance of consistency with the reference set when assessing accuracy. Furthermore, our analysis shows the benefit of using multi-body inverse kinematics procedure over treating inverse kinematics separately for each segment when dealing with inaccurate visual-inertial measurement units data.

This study investigates the possibility of estimating body segment inertial parameters (BSIP) and performing human dynamics analysis using embedded sensors. Affordable embedded inertial measurement units and instrumented force-sensing insoles have not yet demonstrated sufficient accuracy for dynamic assessments of motions in sports or rehabilitation tasks when compared to laboratory-grade solutions, such as marker based stereophotogrammetric systems and force plates. In this paper, we developed a BSIP identification pipeline to estimate inertial parameters among ten healthy young volunteers. Once these parameters are properly identified, several comparisons can be made. First, for the external wrench, we compare estimates derived from different kinematic modalities coupled with either identified BSIP or anthropometric tables against force plate measurements. Second, for joint torques, we compare estimates using embedded kinematics and either identified BSIP or anthropometric tables to the best available reference, which comes from marker-based stereophotogrammetric systems combined with identified BSIP. For validation, in the Y-Balance postural test, comparing external wrench estimates using kinematics from embedded inertial measurement units and identified BSIP to force plate measurements revealed a root mean square error of 5.9N for forces and 18.0N.m for moments, which corresponds to a large center of pressure position error of 3.2cm. Overall, using identified BSIP reduced the normalized root mean square error for joint torques by 6.5% compared to using anthropometric tables, suggesting that kinematic errors from embedded inertial measurement units cannot be entirely compensated.

Human gait patterns have been intensively studied, both from medical and engineering perspectives, to understand and compensate pathologies. However, the muscle-force sharing problem is still debated as acquiring individual muscle force measurements is challenging, requiring the use of invasive devices. Recent studies, using various objective functions, suggest muscle-force sharing may result from an optimization process. This study proposes using inverse optimal control to identify an objective function. Two popular methods of inverse optimal control, bilevel and inverse Karush-Kuhn-Tucker, were investigated. The identified objective functions were then used to predict muscle forces during gait, and their performances were compared to an exhaustive list of biological cost functions from the literature. The best prediction was achieved by the bilevel inverse optimal control method, with a root-mean-squared error of 176 N (162 N) and a correlation coefficient of 0.76 (0.68) for the stance (swing) phase of the gait cycle. These muscle force predictions were thereafter used to compute joint stiffness, exhibiting an average root-mean-square error of 42 Nm.rad $^{-1}$ and a correlation coefficient of 0.90 when compared to the reference. The bilevel method's prevalence in terms of robustness over inverse Karush-Kuhn-Tucker was demonstrated on human data and explained on a toy example.

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement.

This study investigates the possibility of estimating lower-limb joint kinematics and meaningful performance indexes for physiotherapists, during gait on a treadmill based on data collected from a sparse placement of new Visual Inertial Measurement Units (VIMU) and the use of an Extended Kalman Filter (EKF). The proposed EKF takes advantage of the biomechanics of the human body and of the investigated task to reduce sensor inaccuracies. Two state-vector formulations, one based on the use of constant acceleration model and one based on Fourier series, and the tuning of their corresponding parameters were analyzed. The constant acceleration model, due to its inherent inconsistency for human motion, required a cumbersome optimisation process and needed the a-priori knowledge of reference joint trajectories for EKF parameters tuning. On the other hand, the Fourier series formulation could be used without a specific parameters tuning process. In both cases, the average root mean square difference and correlation coefficient between the estimated joint angles and those reconstructed with a reference stereophotogrammetric system was 3.5deg and 0.70, respectively. Moreover, the stride lengths were estimated with a normalized root mean square difference inferior to 2% when using the forward kinematics model receiving as input the estimated joint angles. The popular gait deviation index was also estimated and showed similar results very close to 100, using both the proposed method and the reference stereophotogrammetric system. Such consistency was obtained using only three wireless and affordable VIMU located at the pelvis and both heels and tracked using two affordable RGB cameras. Being further easy-to-use and suitable for applications taking place outside of the laboratory, the proposed method thus represents a good compromise between accurate reference stereophotogrammetric systems and markerless ones for which accuracy is still under debate.

This study aims at developing and evaluating an affordable and user-friendly motion capture system for human upper limbs' joint kinematics estimation. The objective is to provide quantitative assessments during the clinical evaluation of poststroke patients performing daily living activities. The proposed system is based on the simultaneous use of affordable inertial measurement units, and a set of augmented reality markers tracked with an affordable RGB camera. Two practical calibration processes were developed to calibrate the sensors modules and then to determine their location on body segments. Then, all measured quantities were fused into a constrained extended Kalman filter based on an upper limbs' biomechanical model. The proposed system was validated with nine healthy volunteers performing five daily living activities. Joint angles estimated using the proposed affordable system were compared with a gold standard stereophotogrammetric system. The results showed a low average rms difference (2.7°) along with a high average correlation (0.87).

(2019). Overview on dynamic identification methods of floating base anthropomorphic structures. Computer Methods in Biomechanics and Biomedical Engineering: Vol. 22, 44th Congress of the Société de Biomécanique, pp. S471-S473.