Simulation of Human Ankle Trajectory during Stance Phase of Gait

A simulation was developed which mimics the human gait characteristics based on the input of an individual’s gait trajectory. This simulation also estimates the impedance of the human ankle based on the ground reaction forces measured by the force plate. This simulation will accept alterations of the following parameters: total body weight, weight of the shank, weight of the foot, trajectories of the shank and foot of the individual and orientation of the force plate, which would generate a new gait trajectory for the ankle during the stance phase of gait. The goal of this simulation was to validate the protocols followed during experiments conducted on human participants to estimate the impedance of the ankle. It also allowed us to understand and explore different system identification methods. The gait data of two individuals measured experimentally was used to build this simulation model. The simulation implements proportional-integral-derivative (PID) control and impedance control to regenerate the ankle trajectories with time-varying impedance of the ankle joint. This model was tested using the trajectories of the shank and foot from two additional individuals and replicated experimentally obtained ankle trajectories of these individuals, with a mean relative error of 0.53±0.3%, 5.74±4.85% and 4.94±3.13%, in ankle translational trajectory and ankle angular trajectories in dorsi-plantarflexion and inversion-eversion respectively

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

© 2017, Springer Nature Singapore Pte Ltd. 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

Estimating the multivariable human ankle impedance in dorsi-plantarflexion and inversion-eversion directions using EMG signals and artificial neural networks

© 2017, Springer Singapore. The use of a suitably designed ankle-foot prosthesis is essential for transtibial amputees to regain lost mobility. A desired ankle–foot prosthesis must be able to replicate the function of a healthy human ankle by transferring the ground reaction forces to the body, absorbing shock during contact, and providing propulsion. During the swing phase of walking, the human ankle is soft and relaxed; however, it hardens as it bears the body weight and provides force for push-off. The stiffness is one of the components of the mechanical impedance, and it varies with muscle activation (Stochastic estimation of human ankle mechanical impedance in medial-lateral direction, 2014, Stochastic estimation of the multivariable mechanical impedance of the human ankle with active muscles, 2010). This study defines the relationship between ankle impedance and the lower extremity muscle activations using artificial neural networks (ANN). We used the Anklebot, a highly backdrivable, safe, and therapeutic robot to apply stochastic position perturbations to the human ankle in the sagittal and frontal planes. A previously proposed system identification method was used to estimate the target ankle impedance to train the ANN. The ankle impedance was estimated with relaxed muscles and with lower leg muscle activations at 10 and 20% of the maximum voluntary contraction (MVC) of each individual subject. Given the root mean squared (rms) of the electromyography (EMG) signals, the proposed ANN effectively predicted the ankle impedance with mean accuracy of 89.8 ± 6.1% in DP and mean accuracy of 88.3 ± 5.7% in IE, averaged across three muscle activation levels and all subjects