CONTROL OF A POWERED ANKLE-FOOT PROSTHESIS: FROM PERCEPTION TO IMPEDANCE MODULATION

Active ankle prostheses controllers are demonstrating gaining smart features to improve the safety and comfort offor users. The perception of user intention to modulate the ankle dynamics is a well-known example of such feature. But not much work focused on the perception of the environment, nor how the environment should be included in the mechanical design and control of the prosthesisprostheses. The proposed work aims to improve the feasibility of integrate the environment perception integration intoto the prostheses controllersler, and to define the desired ankle dynamics, as mechanical impedance, duringof the human walk on different environmental settings. As a preliminary work on environment perception, a vision system was developed that can estimate the ground profileslope and height. The desired prosthesis impedance dynamics is was defined as the dynamics mechanical impedance of a healthy ankle; , therefore,which required the a system identification methodof the human ankle was developed. Simulations showed the inertia parameters of a rigid bodymockup foot can be estimated. Further experiments will show the accuracy of environment perception and of the impedance estimation

ESTIMATION AND PREDICTION OF THE HUMAN GAIT DYNAMICS FOR THE CONTROL OF AN ANKLE-FOOT PROSTHESIS

With the growing population of amputees, powered prostheses can be a solution to improve the quality of life for many people. Powered ankle-foot prostheses can be made to behave similar to the lost limb via controllers that emulate the mechanical impedance of the human ankle. Therefore, the understanding of human ankle dynamics is of major significance. First, this work reports the modulation of the mechanical impedance via two mechanisms: the co-contraction of the calf muscles and a change of mean ankle torque and angle. Then, the mechanical impedance of the ankle was determined, for the first time, as a multivariable and time-varying system. These findings reveal the importance of recognizing the state of the user during the gait when the user interacts with the environment. In addition to studying the ankle impedance, a wearable device was designed and evaluated to further the studies on robotic perception for ankle-foot prostheses. This device is capable of characterizing the ground environment and estimating the gait state using visual-inertial sensors. Finally, this study contributes to the field of ankle-foot prostheses by identifying the mechanical behavior of the human ankle and developing a platform to test perception algorithms for the control of robotic prostheses

GUPPIE, underwater 3D printed robot a game changer in control design education

This paper presents innovative strategies to teach control and robotic concepts. These strategies include: 1) a real world focus on social/environmental contexts that are meaningful and “make a difference”; 2) continuous design potential and engagement through use of a platform that integrates design with engineering; 3) mission-based versus application-based approaches, where meaningful application justifies the process; and 4) hands-on, inquiry-based problem-solving. For this purpose a Glider for Underwater Problem-solving and Promotion of Interest in Engineering or “GUPPIE” platform and its simulator were utilized. GUPPIE is easy and inexpensive to manufacture, with readily available lightweight and durable components. It is also modular to accommodate a variety of learning activities. This paper describes how GUPPIE and its interdisciplinary nature was used as a pedagogical platform for teaching core control concepts for different age groups. The activities are designed to attract the interest of students as early as middle school and sustain their interest through college. The game changing aspect of this approach is scaffolded learning and the fact that the students will work with the same platform while progressing through the concepts

Towards a generalized model of multivariable ankle impedance during standing based on the lower extremity muscle EMG

The ankle mechanical impedance of healthy subjects was estimated during the standing pose while they co-contracted their lower-leg muscles. Subsequently, the impedance parameters were modeled as a function of the level of co-contraction using machine learning regression methods. From the experimental results, the average ankle stiffness coefficients in dorsi-plantar flexion (DP) showed more dependence to the muscle contraction than stiffness in inversion-eversion (IE): 4.6 Nm/rad per %MVC (percent of the maximum voluntary contraction) and 1.1 Nm/rad per %MVC, respectively. To accurately estimate the ankle impedance parameters as a function of the electromyography (EMG) signals, multiple EMG feature selection methods, regression models, and types of models were evaluated. Using a 1-vs-All model validation approach, the best regression model to fit the stiffness and damping in DP was the Least Square method with Regularization, and the best IE stiffness was the Gaussian Process Regression. No model was able to estimate the IE damping well, possibly because this parameter is not modulated with a changing co-contraction of the lower-leg muscles

Design and preliminary evaluation of a two DOFs cable-driven ankle-foot prosthesis with active dorsiflexion-plantarflexion and inversion-eversion

© 2016 Ficanha, Ribeiro, Dallali and Rastgaar. This paper describes the design of an ankle-foot robotic prosthesis controllable in the sagittal and frontal planes. The prosthesis was designed to meet the mechanical characteristics of the human ankle including power, range of motion, and weight. To transfer the power from the motors and gearboxes to the ankle-foot mechanism, a Bowden cable system was used. The Bowden cable allows for optimal placement of the motors and gearboxes in order to improve gait biomechanics such as the metabolic energy cost and gait asymmetry during locomotion. Additionally, it allows flexibility in the customization of the device to amputees with different residual limb sizes. To control the prosthesis, impedance controllers in both sagittal and frontal planes were developed. The impedance controllers used torque feedback from strain gages installed on the foot. Preliminary evaluation was performed to verify the capability of the prosthesis to track the kinematics of the human ankle in two degrees of freedom (DOFs), the mechanical efficiency of the Bowden cable transmission, and the ability of the prosthesis to modulate the impedance of the ankle. Moreover, the system was characterized by describing the relationship between the stiffness of the impedance controllers to the actual stiffness of the ankle. Efficiency estimation showed 85.4% efficiency in the Bowden cable transmission. The prosthesis was capable of properly mimicking human ankle kinematics and changing its mechanical impedance in two DOFs in real time with a range of stiffness sufficient for normal human walking. In dorsiflexion-plantarflexion (DP), the stiffness ranged from 0 to 236 Nm/rad and in inversion-eversion (IE), the stiffness ranged from 1 to 33 Nm/rad

A multi-level motion controller for low-cost Underwater Gliders

An underwater glider named ROUGHIE (Research Oriented Underwater Glider for Hands-on Investigative Engineering) is designed and manufactured to provide a test platform and framework for experimental underwater automation. This paper presents an efficient multi-level motion controller that can be used to enhance underwater glider control systems or easily modified for additional sensing, computing, or other requirements for advanced automation design testing. The ultimate goal is to have a fleet of modular and inexpensive test platforms for addressing the issues that currently limit the use of autonomous underwater vehicles (AUVs). Producing a low-cost vehicle with maneuvering capabilities and a straightforward expansion path will permit easy experimentation and testing of different approaches to improve underwater automation

Locomotion Envelopes for Adaptive Control of Powered Ankle Prostheses

© 2018 IEEE. In this paper we combine Gaussian process regression and impedance control, to illicit robust, anthropomorphic, adaptive control of a powered ankle prosthesis. We learn the non-linear manifolds which guide how locomotion variables temporally evolve, and regress that surface over a velocity range to create a manifold. The joint set of manifolds, as well as the temporal evolution of the gait-cycle duration is what we term a locomotion envelope. Current powered prostheses have problems adapting across speeds. It is likely that humans rely upon a control strategy which is adaptable, can become more robust and accurate with more data and provides a nonparametric approach which allows the strategy to grow with the number of observations. We demonstrate such a strategy in this study and successfully simulate locomotion well beyond our training data. The method we propose is based on common physical features observed in numerous human subjects walking at different speeds. Based on the derived locomotion envelopes we show that ankle power increases monotonically with speed among all subjects. We demonstrate our methods in simulation and human experiments, on a powered ankle foot prosthesis to demonstrate the effectiveness of the method