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

Evaluating Optimality in the Control of Wearable Robotic Devices

Lower-limb wearable robots, such as prostheses and exoskeletons, have the potential to fundamentally transform the mobility of millions of able-bodied and disabled individuals during work, recreation, and/or rehabilitation. Researchers have developed high-performance robotic devices that are lightweight and autonomous, yet we have not seen their widespread adoption as clinical or commercial solutions. One major factor delaying the dissemination of this technology is that our control of these devices remains limited, especially outside controlled laboratory settings. While many adaptive control strategies have been proposed and tested for wearable robotic systems, it is still an open question as to how to provide users with optimal assistance. This is a challenge because a clear definition of 'optimal assistance' has not yet been established, due to the various goals of assistive devices and the complex interactions between robotic assistance and human physiology. Furthermore, it has been difficult to move these systems outside the laboratory environment due to constraints imposed by tethered hardware and sensing equipment, as well as the difficulty associated with quantifying non-steady-state activities. To address this challenge, the goal of the work presented in this dissertation is to further our understanding of how to provide users with optimal assistance from robotic exoskeletons and prostheses outside the laboratory environment. The first theme of this dissertation is the translation of experimental methods (e.g., human-in-the-loop optimization) and measurement tools outside the laboratory environment. The second theme is the evaluation of context-specific objectives for the optimization of wearable robotic devices (e.g., metabolic cost, user preference) in clinical and research settings. This dissertation comprises four primary projects. First, I evaluated the impact that changing the power setting of the BiOM commercial powered ankle prosthesis has on the metabolic cost of transport of individuals with transtibial amputation. This work informs clinical tuning and prescription practices by revealing that, to minimize their metabolic energy consumption, individuals require a higher power setting than the setting chosen by the prosthetist to approximate biological ankle kinetics. Second, I investigated an alternative method for estimating instantaneous metabolic cost using portable wearable sensors. The goal of this project was to accurately estimate instantaneous metabolic cost without relying on indirect calorimetry, and thus enable the use of human-in-the-loop optimization algorithms outside the laboratory environment. I utilized linear regression algorithms to predict instantaneous metabolic cost from physiological signals, and systematically compared a large set of signals to determine which sensor signals contain the highest predictive ability, robust to unknown subjects or tasks. In the third project, I built upon this work and demonstrated that including the derivatives of physiological signals in the linear regression algorithm could improve both the speed and accuracy of instantaneous metabolic cost prediction from portable sensors. Finally, I investigated user preference as an objective for the control of lower-limb robotic exoskeletons. In this work, I demonstrated that exoskeleton users can quickly and precisely identify unique preferences in the characteristics of their ankle exoskeleton assistance in two dimensions simultaneously, and that preference changes with speed, exposure, and prior experience. These results provide insight into how users interact with exoskeletons, and establish important benchmarks for researchers, designers, and future consumers. Together, these four projects evaluate the notion of optimality in the control of wearable robotic systems and lay the foundation for translating optimization protocols outside the laboratory environment.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169808/1/kaingr_1.pd

Evaluation of transfemoral prosthesis performance control using artificial neural network controllers

Transfemoral (Above-knee) amputation of the leg of an individual as a result of traumatic injury or due to complications arising out of diabetes or vascular disorders is a common occurrence worldwide. Following the surgical amputation procedure, the subject is fitted with prosthetic leg to help regain mobility. Prosthetic sockets are designed to transfer the body weight to the leg during locomotion. During normal human gait, the lower limbs perform four major functions: balance, positioning, support, and power. Prosthetic legs currently available in the market are mostly passive devices that provide limited support and functionality during walking. These devices also have limited adaptability during walking or to enable a more active lifestyle. The common problems of the existing above-knee prosthesis for the unilateral amputees include asymmetry between motion of the prosthetic leg with the intact leg, reduced speed along with increased energy expenditure. Not only that, but there are also different types of forces, counter forces and errors associated with gait which was ignored in some active prosthesis designs. If these technical problems are left un-addressed, they may end up with secondary medical issues requiring further surgery. While it is desirable for the prosthetic limb to have similar or close efficiency or tracking to the intact limb, it is more important for the prosthetic leg to be able to replicate the movement of a normal human leg as much as possible. Most of the studies earlier were limited to pathological gait tests in laboratory environments using inertial sensor/motion trackers which restricted the mobility of the individuals. Recently, smarter data acquisition systems are designed to capture the human locomotion in an easier and effective way. Combination of these factors result in greater advancement of prosthetic research. Prior research in lower-limb amputee gait has focused mostly trans-tibial (below knee) amputees as they are the highest in number. In general, available prostheses for people with lower limb amputation are primarily passive devices whose performance cannot be adjusted or optimized to meet the requirements of different users. The adverse complications of wearing poorly functioning prosthetic devices include asymmetric gait, increased metabolic energy consumption, limited blood flow, instability, sores, and joint pain. The amputees might have to undergo further joint (knee/hip) replacement procedure and that increases the chance of the increased number of trans femoral amputee in the long run. There exists a high and increasing demand for an advanced prosthetic foot that is comfortable and able to replicate the function of the biological foot. Trans-femoral amputees are the second highest and the research is more challenging as the amputees lost two of their vital joints (ankle and knee). So, to design an efficient prosthetic ankle-knee system, (including all the challenges for transtibial amputees) it is very important to consider the coupling effects of the two joints and different associated errors, or force associated with the gait like ground reaction force. Currently available prosthetic knees are either simple mechanical hinges or sophisticated computer controlled. Development of active powered prosthetic knees (focused on the control with little emphasis) results in uncomfortable, low efficient, low energy consuming device. The inherent nonlinearities of the actuators make it difficult to control. Again, interaction forces between residual limb and the socket are dynamic in nature and are a result of gait pattern of individuals, interaction of the feet with the terrain, and the transfer of rest of the body weight during gait. These factors made the prosthetic device control and design advancement challenging for researchers. Earlier literatures address assessing gait symmetry, movement of the healthy joints, activities of the residual muscles and the metabolic energy consumption in individuals who had undergone traditional amputation. There were research studies done showing considerable residual muscle activity in the transtibial and transfemoral amputees and minimal or random muscle activity based on the co-relation between residuum socket interface (RSI) force and EMG to the type of gait. These forces are a source of interest for researchers to investigate for better controlling. Adaptive controllers like PD, PID and combinations are used in the development of active prosthetic devices. But PID and other traditional adaptive controllers cannot handle these nonlinearities and challenges of human locomotion properly. Moreover, most of the designs do not have consistent performance over the total gait cycle or consecutive steps. All prostheses require some sort of stability mechanism, either manual or a weight-activated locking system. The main joints made of mechanical hinges should control the flexion and extension motion to mimic human gait. For unilateral amputee, the development of Artificial optimized neural network controller is important in this regard as it can train the neurons with the input data from the intact leg and mimic similar trajectory for the residual limb to follow. This dissertation addresses the limitations of traditional controllers in an orderly fashion by building a strong platform to develop intelligent knee-ankle prosthesis system. The following are the key steps adopted in this dissertation. • First, a mathematical model will be developed for a leg movement during normal gait. Algorithms for gait analysis will be developed to study the gait of people with above-knee amputation in real time during work-related activities. Simulations will be done to observe the performance of the controller. • A more reliable and realistic learning-based control strategy will be developed to adaptively compensate for the unknown, changing ankle-knee dynamics and drive the prosthetic ankle-knee joint along the desired trajectories. Different combinations of control parameters will be changed to see the performance improvement and error reduction. Comparative results will be shown for different controllers. • Finally, a framework for experimental transfemoral amputee gait study will be proposed to collect data using force sensors and EMG sensors attached to the residual limbs and muscles during work related activities and normal gait. It is anticipated that the learning capabilities of the control strategies will enable the prosthetic ankle-knee joints to not only replicate the movement of the healthy knee-ankle system, but also improve the stability of the gait and optimize the performance to a great extent. Learning-based control of the prosthetic ankle-knee joint algorithms used here consider the ankle-knee dynamics, foot-ground interaction, and the movement of the rest of the body to make it appropriate to be used for transfemoral unilateral amputee. The first strategy uses an artificial neural network-based controller to learn the unknown and changing dynamics of the ankle-knee joint and to track a desired ankle knee displacement profile. In the subsequent strategies, the neural dynamic programming-based controller is improvised by increasing the number of neurons and other parameters, comparative performance was shown for two joints also. Later a centralized controller is used to control both the joints. Additional PID is used and comparative analysis between controller schemes are presented to have a balanced and better control. Actual gait data (obtained from the healthy human subjects) of this dissertation is used to study the effectiveness of the controller. It will be interesting to see the performance of the adaptive neural network controller for unilateral transfemoral amputee with changes in terrain and in user requirements. It is anticipated that the strategy developed in this dissertation will help build an intelligent prosthetic system that can significantly improve the mobility and long-term health of people with lower limb amputation followed by proper rehabilitation

Simulation And Control At the Boundaries Between Humans And Assistive Robots

Human-machine interaction has become an important area of research as progress is made in the fields of rehabilitation robotics, powered prostheses, and advanced exercise machines. Adding to the advances in this area, a novel controller for a powered transfemoral prosthesis is introduced that requires limited tuning and explicitly considers energy regeneration. Results from a trial conducted with an individual with an amputation show self-powering operation for the prosthesis while concurrently attaining basic gait fidelity across varied walking speeds. Experience in prosthesis development revealed that, though every effort is made to ensure the safety of the human subject, limited testing of such devices prior to human trials can be completed in the current research environment. Two complementary alternatives are developed to fill that gap. First, the feasibility of implementing impulse-momentum sliding mode control on a robot that can physically replace a human with a transfemoral amputation to emulate weight-bearing for initial prototype walking tests is established. Second, a more general human simulation approach is proposed that can be used in any of the aforementioned human-machine interaction fields. Seeking this general human simulation method, a unique pair of solutions for simulating a Hill muscle-actuated linkage system is formulated. These include using the Lyapunov-based backstepping control method to generate a closed-loop tracking simulation and, motivated by limitations observed in backstepping, an optimal control solver based on differential flatness and sum of squares polynomials in support of receding horizon controlled (e.g. model predictive control) or open-loop simulations. v The backstepping framework provides insight into muscle redundancy resolution. The optimal control framework uses this insight to produce a computationally efficient approach to musculoskeletal system modeling. A simulation of a human arm is evaluated in both structures. Strong tracking performance is achieved in the backstepping case. An exercise optimization application using the optimal control solver showcases the computational benefits of the solver and reveals the feasibility of finding trajectories for human-exercise machine interaction that can isolate a muscle of interest for strengthening

Adaptive Controllers for Assistive Robotic Devices

Lower extremity assistive robotic devices, such as exoskeletons and prostheses, have the potential to improve mobility for millions of individuals, both healthy and disabled. These devices are designed to work in conjunction with the user to enhance or replace lost functionality of a limb. Given the large variability in walking dynamics from person to person, it is still an open research question of how to optimally control such devices to maximize their benefit for each individual user. In this context, it is becoming more and more evident that there exists no "one size fits all" solution, but that each device needs to be tuned on a subject-specific basis to best account for each user's unique gait characteristics. However, the controllers that run in the background of these devices to dictate when and what type of actuation to deliver often have up to a hundred different parameters that can be tuned on a subject-specific basis. To hand tune each parameter can be an extremely tedious and time consuming process. Additionally, current tuning practices often rely on subjective measures to inform the fitting process. To address the current obstacles associated with device control and tuning, I have developed novel tools that overcome some of these issues through the design of control architectures that autonomously adapt to the user based upon real-time physiological measures. This approach frames the tuning process of a device as a real-time optimization and allows for the device to co-adapt with the wearer during use. As an outcome of these approaches, I have been able to investigate what qualities of a device controller are beneficial to users through the analysis of whole body kinematics, dynamics, and energetics. The framework of my research has been broken down into four major projects. First, I investigated how current standards of processing and analyzing physiological measures could be improved upon. Specifically, I focused on how to analyze non-steady-state measures of metabolic work rate in real time and how the noise content of theses measures can inform confidence analyses. Second, I applied the techniques I developed for analyzing non-steady-state measures of metabolic work rate to conduct a real-time optimization of powered bilateral ankle exoskeletons. For this study I employed a gradient descent optimization to tune and optimize an actuation timing parameter of these simple exoskeletons on a subject-specific basis. Third, I investigated how users may use an adaptive controller where they had a more direct impact on the adaptation via their own muscle recruitment. In this study, I designed and tested an adaptive gain proportional myoelectric controller with healthy subjects walking in bilateral ankle exoskeletons. Through this work I showed that subjects adapted to using increased levels of total ankle power compared to unpowered walking in the devices. As a result, subjects decreased power at their hip and were able to achieve large decreases in their metabolic work rate compared to unpowered walking. Fourth, I compared how subjects may use a controller driven by neural signals differently than one driven by mechanically intrinsic signals. The results of this project suggest that control based on neural signals may be better suited for therapeutic rehabilitation than control based on mechanically intrinsic signals. Together, these four projects have drastically improved upon subject-specific control of assistive devices in both a research and clinical setting.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144029/1/jrkoller_1.pd

Improving Intelligence of Robotic Lower-Limb Prostheses to Enhance Mobility for Individuals with Limb Loss

The field of wearable robotics is an emerging field that seeks to create smarter and intuitive devices that can assist users improve their overall quality of life. Specifically, individuals with lower limb amputation tend to have significantly impaired mobility and asymmetric gait patterns that result in increased energy expenditure than able-bodied individuals over a variety of tasks. Unfortunately, most of the commercial devices are passive and lack the ability to easily adapt to changing environmental contexts. Powered prostheses have shown promise to help restore the necessary power needed to walk in common ambulatory tasks. However, there is a need to infer/detect the user's movement to appropriately provide seamless and natural assistance. To achieve this behavior, a better understanding is required of adding intelligence to powered prostheses. This dissertation focuses on three key research objectives: 1) developing and enhancing offline intent recognition systems for both classification and regression tasks using embedded prosthetic mechanical sensors and machine learning, 2) deploying intelligent controllers in real-time to directly modulate assistive torque in a knee and ankle prosthetic device, and 3) quantifying the biomechanical and clinical effects of a powered prosthesis compared to a passive device. The findings conducted show improvement in developing powered prostheses to better enhance mobility for individuals with transfemoral amputation and show a step forward towards clinical acceptance.Ph.D