Mo Rastgaar

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

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.

Correlation Between Ankle Impedance and EMG Signals

The correlation of the lower-leg muscle contraction to the ankle impedance of unimpaired subjects is studied. Each subject participated in 5 experimental trials, each with a different co-contraction level: 0%, 10%, 20%, 30%, and 40% of their maximum voluntary contraction (MVC). A linear model is developed to relate the muscle contraction and the ankle impedance. Next, an ANOVA test is used to verify the significance of the parameters. Low correlation is found on the inversion-eversion degree-of-freedom of the ankle, suggesting non-linear models might be more effective in describing this relationship.

Gait Emulator for Evaluation of a Powered Ankle-Foot Prosthesis

In this paper we present an enhanced gait emulator and a novel hybrid control system to test powered ankle-foot prostheses with two degrees of freedom in the sagittal and frontal planes. The gait emulator is a nonlinear and non-smooth system that has to follow a precisely timed set of phases to achieve a human-like periodic gait. Despite the complexity and parameter uncertainties of this five degrees of freedom system, our proposed hybrid control system simplifies the walking control by use of state triggered kinematic events. The control system works in closed loop with kinematic event detection to ensure robust and repeatable walking tests as design parameters are varied. The developed gait emulator can be used to test the prosthesis under various loading conditions and walking speeds.Copyright

Co-robotics hands-on activities: A gateway to engineering design and STEM learning

Abstract This paper presents the effect of meaningful learning contexts and hands-on activities, facilitated using two robots that work with people (co-robots), in broadening and sustaining pre-college student engagement in Science, Technology, Engineering, and Mathematics (STEM). The two co-robots are: (1) a Glider for Underwater Problem-solving and Promotion of Interest in Engineering or GUPPIE and (2) a Neurally controlled manipulator called Neu-pulator. The co-robots are easy and inexpensive to manufacture, with readily available lightweight and durable components. They are also modular to accommodate a variety of learning activities that help young students to learn crosscutting concepts and engineering practice. The early assessment results show that students’ interests in activities related to robotics depend on their perception of the difficulty and their confidence level. The key is to start early when the students are young. The challenge is to break the barriers and define tasks as fun activities with a learn and play approach that can be rewarding. In this work, using a meaningful context – as in co-robots that help humans – in a hands-on project-based program that integrates different aspect of design, science, and technology is found effective in increasing students’ enthusiasm and participation. The co-robots and the hands-on activities can be easily adopted in classrooms by teachers with no engineering background who seek innovative ways to connect interdisciplinary core ideas and standards to the concepts they need to teach.