337 research outputs found

    Nonlinear Elastic Material Property Estimation of Lower Extremity Residual Limb Tissues

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    The interface stresses between the residual limb and prosthetic socket have been studied to investigate prosthetic fit. Finite-element models of the residual limb-prosthetic socket interface facilitate investigation of the mechanical interface and may serve as a potential tool for future prosthetic socket design. However, the success of such residual limb models to date has been limited, in large part due to inadequate material formulations used to approximate the mechanical behavior of residual limb soft tissues. Nonlinear finite-element analysis was used to simulate force-displacement data obtained during in vivo rate-controlled (1, 5, and 10 mm/s) cyclic indentation of the residual limb soft tissues of seven individuals with transtibial amputation. The finite-element models facilitated determination of an appropriate set of nonlinear elastic material coefficients for bulk soft tissue at discrete clinically relevant test locations. Axisymmetric finite-element models of the residual limb bulk soft tissue in the vicinity of the test location, the socket wall and the indentor tip were developed incorporating contact analysis, large displacement, and large strain, and the James-Green-Simpson nonlinear elastic material formulation. Model dimensions were based on medical imaging studies of the residual limbs. The material coefficients were selected such that the normalized sum of square error (NSSE) between the experimental and finite-element model indentor tip reaction force was minimized. A total of 95% of the experimental data were simulated using the James-Green-Simpson material formulation with an NSSE less than 5%. The respective James-Green-Simpson material coefficients varied with subject, test location, and indentation rate. Therefore, these coefficients cannot be readily extrapolated to other sites or individuals, or to the same site and individual some time after testing

    Deep Reinforcement Learning for Physics-Based Musculoskeletal Simulations of Healthy Subjects and Transfemoral Prostheses’ Users During Normal Walking

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    This paper proposes to use deep reinforcement learning for the simulation of physics-based musculoskeletal models of both healthy subjects and transfemoral prostheses’ users during normal level-ground walking. The deep reinforcement learning algorithm is based on the proximal policy optimization approach in combination with imitation learning to guarantee a natural walking gait while reducing the computational time of the training. Firstly, the optimization algorithm is implemented for the OpenSim model of a healthy subject and validated with experimental data from a public data-set. Afterwards, the optimization algorithm is implemented for the OpenSim model of a generic transfemoral prosthesis’ user, which has been obtained by reducing the number of muscles around the knee and ankle joints and, specifically, by keeping only the uniarticular ones. The model of the transfemoral prosthesis’ user shows a stable gait, with a forward dynamic comparable to the healthy subject’s, yet using higher muscles’ forces. Even though the computed muscles’ forces could not be directly used as control inputs for muscle-like linear actuators due to their pattern, this study paves the way for using deep reinforcement learning for the design of the control architecture of transfemoral prostheses

    Powered Transtibial Prosthetic Device Control System Design, Implementation and Testing

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    A powered lower limb prosthesis, which consists of a four bar mechanism, a torsional spring and a brushed DC motor, was previously designed and fabricated. To regulate the motor power input, a two level controller was proposed and built. The control algorithm includes a higher level finite state controller and lower level PID controllers. To implement the control system, a digital signal processor (DSP) control board and MATLAB Simulink were used to realize the higher level control and a DC motor controller was used to realize the lower level PID control. Sensors were selected to provide the required feedback. The entire control system was implemented on a convenient to carry backpack. Amputee subject testing was performed to obtain some experimental verification of the design. The results showed that the control system performed consistently with the designed control algorithm and did assist in the amputee’s walking. Compared to a currently available powered prosthesis, this control is simple in structure and able to mimic the nonlinear behavior of the ankle closely
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