Deanna H. Gates, Ph.D. - US grants

State-of-the-art lower-limb prostheses are generally passive devices that do not provide any active power to their user. As a consequence, people with amputations must expend substantially more energy when walking. Recent advances in prostheses have addressed these shortcomings by including batteries and motors to provide active power. Unlike traditional prostheses, the software of these active devices needs to be "tuned" to each person. This project will investigate ways to improve this tuning process and thus enhance the performance of people using powered prostheses. To this end, the study team will systematically change device settings while measuring the energy needed to walk and the symmetry of the resulting walking motion. Additionally, the researchers will investigate the use of a computer program that will automate this process. That is, the computer will take repeated measurements of walking performance and will automatically change device settings until optimal values are determined. All this happens while the user is walking with the prosthesis. The result can positively affect the lives of about 1.6 million people that are living with limb loss. With a well working powered prosthesis, amputees can walk longer and in a more natural fashion. This may also have secondary health benefits, such as a smaller risk of heart disease, or a reduction in pain.

This project establishes objectivity in the determination of device settings for powered prostheses. It will carefully quantify the influence of controller parameters, such as power magnitude and the time at which power is supplied, onto the performance of powered prostheses. It will determine measures of metabolic effort, muscle activity, kinematics, kinetics, and subjective feedback. All studies will be conducted with individuals with transtibial amputation. In addition, the researchers will investigate the potential of an optimization algorithm that automatically finds controller parameters through the minimization of metabolic effort. To achieve this in real time with the human body being "in the loop," the researchers will investigate advanced methods for signal processing and optimization. One of the key innovations in this context is to explicitly take into account the metabolic dynamics of each individual subject. This allows making use of all the metabolic measurements -- even those taken before steady-state is reached. This technique greatly accelerates the process of indirect calorimetry and enables the automated tuning process. From the result, one will be able to determine how to appropriately identify optimal parameter settings of powered prostheses and how much improvement they provide compared to manual tuning.

Project Summary Lower limb assistive robotic devices, such as active prosthesis, orthoses, and exoskeletons have the potential to restore function for the millions of Americans who experience mobility challenges due to injury and disability. Since individuals with mobility challenges have an increased energetic cost of transport, the benefit of such assistive devices is commonly assessed via the reduction in the metabolic work rate of the individual who is using the device. Currently, metabolic work rate can only be obtained in a laboratory environment, using breath-by-breath measurements of respiratory gas analysis. To obtain a single steady state data point of metabolic work rate, multiple minutes of data must be collected, since the signals are noisy, sparsely sampled, and dynamically delayed. In addition, the user has to wear a mask and bulky equipment, further restricting the applicability of the method on a larger scale. We propose an improved way to obtain such estimates of metabolic work rate in real-time. Aim 1 will determine salient signal features and characterize the dynamics of sensing metabolic work rate from a variety of physiological sensor signals. Aim 2 will use advanced sensor fusion and machine learning techniques to accurately predict instantaneous energy cost in real-time from multiple physiological signals without relying on a metabolic mask. Aim 3 will use the obtained real-time estimates to optimize push-off timing for an active robotic prosthesis. The resulting methods will enable an automated and continuous evaluation of assistive robotic devices that can be realized outside the laboratory and with simple wearable sensors. This automated evaluation will enable devices, such as active prostheses, orthoses, or exoskeletons, that can self-monitor their performance, optimize their own behavior, and continuously adapt to changing circumstances. This will open up a radically new way of human-robot- interaction for assistive devices. It will greatly increase their clinical viability and enable novel advanced controllers and algorithms that can improve device performance on a subject specific basis.

A wide variety of prostheses are available to persons with upper-limb loss. These include devices that simply open and close the hand to those with multiple grip options and wrist movement. However, it is difficult to determine the optimal device for an individual because there is no objective standard. There are two main reasons for this shortcoming. First, there is currently no means to evaluate the effect of individual prosthetic features on user performance. For example, it is currently not possible to solely increase prosthetic wrist motion without changing the system weight and volume. Accordingly, if the patient rejects the prosthesis, it is unclear whether they are rejected due to difficulties in use or because of the weight. Second, it can be difficult to assess performance with upper limb prostheses as there is no single task that is representative of all upper limb activities of daily living. The goal of this proposal is to develop a new metric, called holistic indicator, which quantifies an individual’s performance and perception of a prosthesis. To make this possible, a cable-actuated prosthetic emulator is employed which can mimic different physical characteristics of the prosthesis and use machine learning to understand the relationship between design characteristics and user performance. The system developed in this proposal will enable the PI Team to find the optimal prosthesis for an individual by understanding their unique robot-human interaction. The research outcomes of this proposal will be disseminated through Wearable Robotics Camp for K-12 students and FEMMES (Females Excelling More in Math, Engineering, and Science) events for female high school students to encourage STEM education among the next generation. The learning experiences of graduate and undergraduate students working on this project will be a unique opportunity to acquire multidisciplinary skill sets, build professional networks through collaboration between faculty and students in robotics, data science, and biomechanics, and foster trans-disciplinary leaders of AI-based wearable robotics.<br/><br/>The goal of this proposal is to design a human-in-the-loop (HITL) framework for prosthetic arm parameter optimization by determining a quantifiable holistic indicator using interpretable machine learning (ML) models. A holistic indicator is a metric for prosthesis optimization that incorporates both physical and cognitive quantitative responses. The innovation of a holistic framework is to reflect multiple critical factors during prosthesis use for optimizing a specific design parameter of interest of the upper limb prosthesis. The framework will be developed by collecting data from individuals with upper limb amputation using a cable-actuated prosthetic emulator arm, Intelligent COnvertible Prosthetic Emulator (ICOPE), developed in the PI’s laboratory. ICOPE features off-board electronics to easily change only specific design parameters through software while maintaining the rest of the design parameters. The data from ICOPE will be used to train interpretable ML models to determine the holistic indicator. The same data set will be collected from non-amputee participants as well, to identify the unique robot-human interaction of amputee participants for personalized prosthetic designs. This project will also include pilot studies utilizing the HITL framework with longer training periods and investigate user perception of the optimized design when the objective metrics will be shared with the user.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.