Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Abstract

The human ankle has a critical role in locomotion and estimating its impedance is essential for human gait rehabilitation. The ankle is the first major joint that regulates the contact forces between the human body and the environment, absorbing shocks during the stance, and providing propulsion during walking. Its impedance varies with the level of the muscle activation. Hence, characterizing the complex relation between the ankle impedance and the lower leg's muscle activation levels may improve our understanding of the neuromuscular characteristics of the ankle. Most ankle-foot prostheses do not have a degree of freedom in the transverse plane, which can cause high amounts of shear stress to be applied to the socket and can lead to secondary injuries. Quantifying the ankle impedance in the transverse plane can guide the design for a variable impedance ankle-foot prosthesis that can significantly reduce the shear stress on the socket. This paper presents the results of applying artificial neural networks (ANN) to learn and estimate the relation between the ankle impedance in the transverse plane under non-load bearing condition using electromyography signals (EMG) from the lower leg muscles. The Anklebot was used to apply pseudorandom perturbations to the human ankle in the transverse plane while the other degrees of freedom (DOF) in the sagittal and frontal planes were constrained. The mechanical impedance of the ankle was estimated using a previously proposed stochastic identification method that describes the ankle impedance as a function of the applied disturbances torques and the ankle motion output. The ankle impedance with relaxed muscles and with the lower leg's muscle activations at 10 and 20% of the maximum voluntary contraction were estimated. The proposed ANN effectively predicts the ankle impedance within 85% accuracy (±5 Nm/rad absolute) for nine out of ten subjects given the root-mean-squared (rms) of the EMG signals. The main contribution of this paper is to quantify the relationship between lower leg muscle EMG signals and the ankle impedance in the transverse plane to pave the way towards designing and controlling this degree of freedom in a future ankle-foot prosthesis.

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

“…In addition, it has been already demonstrated that neurological diseases lead to a significant deterioration of ankle impedance, with respect to healthy subjects [21]. The application of dynamic perturbations to the examined anatomical joint and successive analysis of torque vs. angle graphs is currently the most widespread methodology for the measurement of the dynamic joint mechanical impedance [20,[22][23][24]. Different studies have been proposed in the literature for the objective measurement of ankle impedance in adult subjects.…”

“…The innovative aspect proposed by the authors is related to the feasibility of impedance evaluation in multiple directions, overcoming the limits of the previously proposed approaches [27][28][29]. Following a similar approach, Dallali et al evaluated ankle impedance in the external-internal direction by analyzing the lower limb muscle activation and by applying an artificial neural network that achieved accuracy of 85% in the impedance estimation [22]. Conversely, in the literature, a limited number of studies have focused on ankle impedance evaluation in children.…”

Estimation of Multi-Directional Ankle Impedance as a Function of Lower Extremity Muscle Activation

“…This work is important in understanding the ankle dynamics, and has the potential to be used for rehabilitation, injury prevention, and the improved control of ankle-foot prostheses. The results presented in this chapter have been published previously [109], [108].…”

“…Little work has been done to explore the relationship between ankle impedance and muscle cocontractions of the agonistic and antagonistic muscles surrounding the ankle, especially during weight-bearing activities such as standing and walking. To address some of these gaps, the work presented in this paper explored the development of EMG-impedance models that can predict ankle impedance in DP, IE, and ML directions [108], [109]. Additionally, these models use predictors from multiple muscles of the lower extremity, as opposed to a single muscle, and examine both non-loaded and various standing scenarios [110], [111].…”