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.
