Impact of Technologies That Personalize Robotic Leg Prostheses for Individuals with Transfemoral Amputation of Varying Mobility
Abstract
Background: The population of individuals with major lower-limb loss is diverse, and one way to distinguish these individuals is by their functional mobility level. This diversity sheds light on the complexity of developing prostheses that generalize across this population, given that the needs of these individuals are likely to be quite different from one another. The diversity of individuals with limb amputation who are Service Men and Women or individuals who are Veterans is similarly high. For example, individuals who sustained amputations in earlier military conflicts, or later in life, are significantly older than individuals who were injured in more recent conflicts. The demands of ambulation within the community can limit mobility. Powered (i.e., robotic) prostheses have the potential to assist individuals with limb loss to increase their mobility and independence. While these devices have incredible potential, objective evidence to support widespread clinical implementation remains insufficient. This proposal is aligned with the FY 2021 PRORP CTRA Focus Area, Prosthetic and Orthotic Devices. This research will develop and test innovative and high-performance control systems and hardware technologies to improve whole-person performance in patients with unilateral transfemoral amputation of varying functional mobility. This multi-PI and multi-site project will test the ability of new control and hardware technologies to scale prosthetic knee and ankle control to each person and context (i.e., personalization). We test the idea that control systems, which continuously estimate user intent and context that are implemented to self-learn, auto- configure, and adapt to an individual can enhance performance during widely varying ambulation scenarios relative to currently prescribed standard-of-care prostheses and do so across a spectrum of patient functional mobility. Objective/Hypothesis: The objectives of this research are to (1) develop personalized prosthesis control systems that deliver personalized assistance to each person and task using continuous estimation of user and context- dependent states through artificial intelligence and real-time optimization and (2) evaluate how these technologies improve patient-perceived, functional and biomechanical outcomes during ambulation in patients of differing mobility levels relative to their performance with standard-of-care devices. We test the central hypothesis that user intent systems that self-learn and adapt to an individual, and real-time optimization techniques that auto-configure device assistance in widely varying ambulation scenarios can improve user performance compared to standard-of-care prescribed prostheses for individuals of varying mobility. Specific Aims: Aim 1 continuously predicts user intent and ambulation context by implementing machine learning algorithms that self-learn individualized mobility patterns of people with transfemoral limb loss. Aim 2 applies algorithms during ambulation that auto-configure how the knee and ankle of an open-source robotic leg prostheses are controlled within a given task for individuals with transfemoral amputation of different mobility. Aim 3 delivers fully integrated control systems of robotic leg prostheses that self-learn an individual’s intention, context, and optimal control parameters and compare self-reported and biomechanical measures of relative efficacy in comparison to standard-of-care prescribed prostheses. Study Design: Our Aims evaluate specific aspects of these advanced control systems for robotic leg prostheses relative to either control systems that do not self-learn or auto-configure their assistance or relative to the prescribed prostheses of individuals with transfemoral amputation who are K2, K3, and K4 ambulators. Subjects will ambulate over various terrains to simulate ambulation conditions they encounter in their community or home. We evaluate user outcomes that are biom
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2211091
Entities
People
- Nicholas Fey
Organizations
- United States Army
- University of Texas at Austin