Personalized Mobility Interventions Using Smart Sensor Resources for Lower Limb Prostheses Users
Abstract
Objective: Our goal is to use multiple assessments of physical and social activity using smart phone and wearable sensors to determine which individuals are not using their prosthesis optimally, what limits their ability or desire to use their prosthesis, and whether targeted interventions can improve prosthesis use and enhance community mobility and social interaction. Rationale: Loss of a leg causes significant disability, affecting performance of daily tasks, leisure activities, and employment - and, for military Service members, the possibility of a return to active duty and deployment. Although a prosthesis is the best way to regain lost function, even with advanced rehabilitation care and the latest devices many people don’t use their prosthesis as much as they could, which limits their mobility and ability to do what they want to do. Reduced physical activity may have secondary effects on health and well-being, reducing quality of life and increasing medical costs. Barriers to prosthesis use include physical and psychological factors. Walking with a prosthesis requires more energy than walking with an intact leg, which may limit how often the prosthesis is used, especially in older or unfit individuals. Use of compensatory walking strategies may result in pain in the back or remaining leg. A poorly fitting prosthesis may cause pain or skin issues that prevent use. Or the person might be embarrassed or depressed about their amputation, afraid of falling, or lack motivation to use their device for desired activities. People are assigned a prosthesis based on a clinician’s assessment of how well they could use it - with little information on how they actually use it in everyday life. Currently there are no good ways to know how well someone uses their prosthesis at home or in the community. Most testing is done in the clinic (which is very different to the person’s home and community) or through questionnaires (which are unreliable because the person may not remember accurately). We have recently developed a smartphone-based monitoring system that can provide rich information about people’s activity, where they go, and how they use their prosthesis in real life, using sensors that are built into the phone. We have developed an app (called CIMON) that can gather data from a variety of sensors and transmit it to a secure server for analysis using machine-learning and data-mining techniques, to provide a well-rounded picture of an individual’s activity. We will use this information, together with standard clinical tests of performance, self-report surveys, and personal interviews to (i) identify individuals who do not use their prosthesis as much as they could and/or don’t achieve their own personal mobility goals, and (ii) identify what is limiting their prosthesis use. We will then provide a targeted intervention to address physical reasons (e.g., refit or repair of the prosthesis with physical therapy), or psychological reasons - using motivational interviewing, a technique used to help people change their behavior, or both interventions together. We will use machine-learning analysis techniques to identify which measures accurately predict prosthesis use at home. Impact, Clinical Applicability, Benefits, and Risks of the Research: We expect this research to identify which clinical measures most accurately predict prosthesis use. This will allow clinicians to more provide more effective care, in providing prostheses and in addressing issues that limit prosthesis use, so that individuals with lower limb amputations are able to fulfill their physical potential and achieve personal mobility goals. Of the estimated 1700 amputations resulting from recent combat, most affect the lower limb, at either the transtibial (41.8%) or transfemoral (34.5%) level. The Veteran population includes individuals who lost a leg in recent or past conflicts, and older individuals. As in the civilian population, most leg ampu
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1820057
Entities
People
- Arun Jayaraman
Organizations
- Shirley Ryan AbilityLab
- United States Army