Determination Of Optimal Counter-Mass Location In Active Prostheses For Transfemoral Amputees To Replicate Normal Swing

Transfemoral amputees suffer the loss of the knee and ankle joints, as well as partial or complete loss of many of the lower extremity muscle groups involved in ambulation. Recent advances in lower limb prostheses have involved the design of active, powered prosthetic knee and ankle-foot components capable of generating knee and ankle torques similar to that of normal gait. The associated onboard motors, conditioning/processing, and battery units of these active components result in increased mass of the respective prosthesis. While not an issue during stance, this increased mass of the prosthesis affects swing. The goal of this study is to develop and validate mathematical models of the transfemoral residual limb and prosthesis, expand these models to include an active ankle-foot, and investigate counter-mass magnitude(s) and location(s) via model optimization that might improve kinematic symmetry during swing. Single- (thigh only, shank only) and multi-segment (combined thigh and shank) optimization of counter-mass magnitudes and locations indicated that a 2.0 kg counter-mass added 8 cm distal and 10 cm posterior to the distal end of knee unit within the shank segment approximated knee kinematics of able-bodied subjects. This location, however, induced artificial hip torques that reduced hip flexion during swing. While such a counter-mass location and magnitude demonstrated theoretical potential, this location is not clinically realistic; mass can only be added within the prosthesis, distal to the residual limb. Clinically realistic counter-masses must also keep the total prosthetic mass to less than 5 kg; greater mass requires supplemental prosthetic suspension, would likely increase energy expenditure during ambulation, and contribute to increased likelihood of fatigue even with active prosthetic components. The ability to simulate the effects of active prosthetic components inclusive of varying placement of battery and signal conditioning units may advance the design of active prostheses that will minimize kinematic asymmetry and result in greater patient acceptance

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Design, Control, and Optimization of Robots with Advanced Energy Regenerative Drive Systems

We investigate the control and optimization of robots with ultracapacitor based regenerative drive systems. A subset of the robot joints are conventional, in the sense that external power is used for actuation. Other joints are energetically self-contained passive systems that use ultracapacitors for energy storage. An electrical interconnection known as the star configuration is considered for the regenerative drives that allows for direct electric energy redistribution among joints, and enables higher energy utilization efficiencies. A semi-active virtual control strategy is used to achieve control objectives. We find closed-form expressions for the optimal robot and actuator parameters (link lengths, gear ratios, etc.) that maximize energy regeneration between any two times, given motion trajectories. In addition, we solve several trajectory optimization problems for maximizing energy regeneration that admit closed-form solutions, given system parameters. Optimal solutions are shown to be global and unique. In addition, closed-form expressions are provided for the maximum attainable energy. This theoretical maximum places limits on the amount of energy that can be recovered. Numerical examples are provided in each case to demonstrate the results. For problems that don\u27t admit analytical solutions, we formulate the general nonlinear optimal control problem, and solve it numerically, based on the direct collocation method. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA manipulator with custom regenerative drives. Power flows, stored regenerative energy and efficiency are evaluated. Experimental results show that when following optimal trajectories, a reduction of about 10-22% in energy consumption can be achieved. Furthermore, we present the design, control, and experimental evaluation of an energy regenerative powered transfemoral prosthesis. Our prosthesis prototype is comprised of a passive ankle, and an active regenerative knee joint. A novel varying impedance control approach controls the prosthesis in both the stance and swing phase of the gait cycle, while explicitly considering energy regeneration. Experimental evaluation is done with an amputee test subject walking at different speeds on a treadmill. The results validate the effectiveness of the control method. In addition, net energy regeneration is achieved while walking with near-natural gait across all speeds

Dynamic Modeling, Parameter Estimation and Control of a Leg Prosthesis Test Robot

Robotic testing can facilitate the development of new concepts, designs and control systems for prosthetic limbs. Human subject test clearances, safety and the lack of repeatability associated with human trials can be reduced or eliminated with automated testing, and test modalities are possible which are dangerous or inconvenient to attempt with patients. This paper describes the development, modeling, parameter estimation and control of a robot capable of reproducing two degree-of-freedom hip motion in the sagittal plane. Hip vertical displacement and thigh angle motion profiles are applied to a transfemoral prosthesis attached to the robot. A treadmill is used as walking surface. Aside from tracking hip motion trajectories, the control system can be used to regulate the contact force between the treadmill and the prosthesis. The paper summarizes the overall development process, with emphasis on the generation of a dynamic model that can be used to design closed-loop motion and force control algorithms

The Effects of the Inertial Properties of Above-Knee Prostheses on Optimal Stiffness, Damping, and Engagement Parameters of Passive Prosthetic Knees

Our research aims to design low-cost, high-performance, passive prosthetic knees for developing countries. In this study, we determine optimal stiffness, damping, and engagement parameters for a low-cost, passive prosthetic knee that consists of simple mechanical elements and may enable users to walk with the normative kinematics of able-bodied humans. Knee joint power was analyzed to divide gait into energy-based phases and select mechanical components for each phase. The behavior of each component was described with a polynomial function, and the coefficients and polynomial order of each function were optimized to reproduce the knee moments required for normative kinematics of able-bodied humans. Sensitivity of coefficients to prosthesis mass was also investigated. The knee moments required for prosthesis users to walk with able-bodied normative kinematics were accurately reproduced with a mechanical system consisting of a linear spring, two constant-friction dampers, and three clutches (R[superscript 2]=0.90 for a typical prosthetic leg). Alterations in upper leg, lower leg, and foot mass had a large influence on optimal coefficients, changing damping coefficients by up to 180%. Critical results are reported through parametric illustrations that can be used by designers of prostheses to select optimal components for a prosthetic knee based on the inertial properties of the amputee and his or her prosthetic leg

A Generalized Method for Predictive Simulation-Based Lower Limb Prosthesis Design

Lower limb prostheses are designed to replace the functions and form of the missing biological anatomy. These functions are hypothesized to improve user outcome measures which are negatively affected by receiving an amputation – such as metabolic cost of transport, preferred walking speed, and perceived discomfort during walking. However, the effect of these design functions on the targeted outcome measures is highly variable, suggesting that these relationships are not fully understood. Biomechanics simulation and modeling tools are increasingly capable of analyzing the effects of a design on the resulting user gait. In this work, prothesis-aided gait is optimized in simulation to reduce both muscle effort and peak loads on the residual limb using a generalized prosthesis model. Compared to a traditional revolute powered ankle joint model, a two degree-of freedom generalized model reduced muscle activations by 50% and peak loads by 15%. Simulated prosthesis behaviors corresponding to the optimal gait patterns were translated into a two degree-of-freedom ankle-foot prosthesis design with powered bidirectional linear translation and plantarflexion. The prototype is capable of delivering up to 171 N-m of plantarflexion torque and 499 N of translation force, with 15° dorsi-/35° plantarflexion and 10 cm translation range of motion. The mass and height of the ankle-foot are 2.29 kg and 19.5 cm, respectively. The mass of the entire system including the wearable offboard system is 8.58 kg. This platform is designed to emulate the behavior of the simulated prosthesis, as well as be configurable to emulate alternate behaviors obtained from simulations with different optimization objectives. The prototype is controlled to replicate simulated walking patterns using a high level finite state controller, mid-level stiffness controller, and low level load controller. Closed loop load control has bandwidth of 15 Hz in translation and 7.2 Hz in flexion. Load tracking during walking with a single able-bodied human subject ranges from 93 to 159 N in translation and 4.6 to 21.3 N-m in flexion. The contribution of this work is to provide a framework for predictive simulation-based prosthesis design, evidence of its practical implementation, and the experimental tools to validate future predictive simulation studies

Sensor-Based Adaptive Control and Optimization of Lower-Limb Prosthesis.

Recent developments in prosthetics have enabled the development of powered prosthetic ankles (PPA). The advent of such technologies drastically improved impaired gait by increasing balance and reducing metabolic energy consumption by providing net positive power. However, control challenges limit performance and feasibility of today’s devices. With addition of sensors and motors, PPA systems should continuously make control decisions and adapt the system by manipulating control parameters of the prostheses. There are multiple challenges in optimization and control of PPAs. A prominent challenge is the objective setup of the system and calibration parameters to fit each subject. Another is whether it is possible to detect changes in intention and terrain before prosthetic use and how the system should react and adapt to it. In the first part of this study, a model for energy expenditure was proposed using electromyogram (EMG) signals from the residual lower-limbs PPA users. The proposed model was optimized to minimize energy expenditure. Optimization was performed using a modified Nelder-Mead approach with a Latin Hypercube sampling. Results of the proposed method were compared to expert values and it was shown to be a feasible alternative for tuning in a shorter time. In the second part of the study, the control challenges regarding lack of adaptivity for PPAs was investigated. The current PPA system used is enhanced with impedance-controlled parameters that allow the system to provide different assistance. However, current systems are set to a fixed value and fail to acknowledge various terrain and intentions throughout the day. In this study, a pseudo-real-time adaptive control system was proposed to predict the changes in the gait and provide a smoother gait. The proposed control system used physiological, kinetic, and kinematic data and fused them to predict the change. The prediction was done using machine learning-based methods. Results of the study showed an accuracy of up to 89.7 percent for prediction of change for four different cases

The Effects of Prosthesis Inertial Properties on Prosthetic Knee Moment and Hip Energetics Required to Achieve Able-bodied Kinematics

There is a major need in the developing world for a low-cost prosthetic knee that enables users to walk with able-bodied kinematics and low energy expenditure. To efficiently design such a knee, the relationship between the inertial properties of a prosthetic leg and joint kinetics and energetics must be determined. In this paper, using inverse dynamics, the theoretical effects of varying the inertial properties of an above-knee prosthesis on the prosthetic knee moment, hip power, and absolute hip work required for walking with ablebodied kinematics were quantified. The effects of independently varying mass and moment of inertia of the prosthesis, as well as independently varying the masses of each prosthesis segment, were also compared. Decreasing prosthesis mass to 25% of physiological leg mass increased peak late-stance knee moment by 43% and decreased peak swing knee moment by 76%. In addition, it reduced peak stance hip power by 26%, average swing hip power by 76%, and absolute hip work by 22%. Decreasing upper leg mass to 25% of its physiological value reduced absolute hip work by just 2%, whereas decreasing lower leg and foot mass reduced work by up to 22%, with foot mass having the greater effect. Results are reported in the form of parametric illustrations that can be utilized by researchers, designers, and prosthetists. The methods and outcomes presented have the potential to improve prosthetic knee component selection, facilitate ablebodied kinematics, and reduce energy expenditure for users of low-cost, passive knees in developing countries, as well as for users of advanced active knees in developed countries.MIT Department of Physics Pappalardo Program (Fellowship)Massachusetts Institute of Technology. Public Service CenterMassachusetts Institute of Technology. Research Support CommitteeNational Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374)MIT Tata Center for Technology and Desig