Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks: a Validation-Based Approach

Abstract

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.

Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression

“…The estimated stiffness resembles the estimate obtained in [6]. Future work should be done to provide a physiological interpretation of the parameters, potentially comparing the results with physiological-based models [3].…”

“…The right foot of the subject was attached to the Achilles Rehabilitation Device (MOOG, Nieuw-Vennep, The Netherlands). The apparatus is a one degree of freedom ankle manipulator which can apply angular perturbations to the ankle joint in the sagittal plane [3]. The foot of the subject was firmly attached to the footplate of the manipulator, such that the angle of the manipulator corresponded to the angle of the ankle joint.…”

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

“…In this context, a pure sensor-based approach would not enable sampling internal neuromuscular variables (i.e., individual muscle force or stiffness) in vivo in the intact moving human in a non-invasive way. On the other hand, the ability of using non-invasive wearable sensor data (e.g., electromyography surface electrodes, thin-film low-profile ultrasonography probes) to drive forward subject-specific neuromuscular models will lead to a framework for deriving high-fidelity estimates of human neuromuscular function [ 141 , 142 ]. This is expected to provide new avenues to inform wearable device controllers of the user’s current physiological state or determine the optimal body postures to prevent musculoskeletal injuries on the long term [ 7 , 8 , 143 ].…”

Estimation of Time-Varying Ankle Joint Stiffness Under Dynamic Conditions via System Identification Techniques

“…The lack of consistency in estimated stiffness may be explained by differences across conditions, in properties such as reflexes, muscle force-length and force-velocity relationships, pennation angle or moment arms. Musculoskeletal models such as EMGdriven models may provide insight in the physiological changes of the aforementioned properties [14].…”