NRI: INT: COLLAB: Accelerating Large-Scale Adoption of Robotic Lower-Limb Prostheses through Personalized Prosthesis Controller Adaptation

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Experiencing a lower limb amputation is a life-changing event. A person with lower-limb amputation relies heavily on a leg prosthesis to support himself/herself and stay mobile. However, most leg prostheses in current use are unpowered, which can cause great inconvenience. For example, these unpowered prostheses cannot propel the amputee users forward in walking, and cannot push them up while standing up or climbing stairs. With the advances in medical robotic technologies, powered robotic leg prostheses are starting to become more common. These powered prostheses can help amputees to walk more easily and naturally, and also provide power to support activities previously unattainable or extremely difficult with existing unpowered prostheses (such as standing up and stair climbing). On the other hand, these robotic prostheses are much more complex than the existing unpowered prostheses, so tuning such a prosthesis to fit an individual user is very challenging and time-consuming, requiring numerous office visits with a clinician over a long period of time. This is one of the main reasons why powered leg prostheses have not gained extensive use among the amputee users.In this project, the research team will help to solve this problem by developing a new method to automate the prosthesis tuning process. The main idea is to use a pendant-like wearable sensor to measure upper body motion, which provides rich information about how well the prosthesis is being controlled to help the user to walk and perform other basic tasks of daily life. The new method will use such information and gradually tune the controller parameters over time without the need for constant clinical supervision. To develop this method, the researchers will study how an experienced prosthetist tunes the prosthesis parameters and develop an algorithm to mimic this process. Furthermore, the upper body motion will be used to infer the amputee user's intention more precisely, so the prosthesis can reliably understand what the user intends to do even if he/she changes the motion pattern while learning to use the robotic prosthesis.By conducting research in this project, the researchers aim to develop a complete Personalized Prosthesis Controller Adaptation (PPCA) system, which provides personalized controller adaption on two levels: 1) automatic motion controller tuning, and 2) automatic intent recognizer adaptation. The researchers anticipate to make significant contributions to the related scientific areas, including: 1) a novel wearable sensor that incorporates an inertial measurement unit (IMU), capacitive sensing (for sensor-torso relative motion), and advanced signal processing to provide reliable trunk motion information; 2) fundamental understanding of robotic prosthesis-assisted amputee locomotion, and how human expertise-based tuning optimizes its gait quality; 3) a novel quasi-supervised adaptation of classifier-based intent recognizer, which provides the advantages of the traditional supervised learning while avoiding its major weakness (highly effective in adapting to changing human conditions, and no repeated training sessions or human-conducted data labeling required). Impacts of this project will also be generated by its various education activities, including the introduction of robotic technologies to the future prosthetic clinicians through a renowned prosthetics and orthotics education program, and the creation of hands-on robotic projects in undergraduate research, which can also serve as important tools in the K-12 outreach to attract children at different age groups to the science and engineering fields.