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Technology and Physical Assessment

The National Institute for Disability, Independent Living, and Rehabilitation Research (NIDILRR) awarded the LiveWell Rehabilitation Engineering Research Center Program (RERC) to a team from Duke University, Shepherd Center, and Northeastern University. This program funds advanced engineering research and development of innovative technologies to solve rehabilitation problems or remove environmental barriers for people with disabilities. A multidisciplinary team of clinicians, engineers and researchers collaborate with Pratt School of Engineering, School of Medicine, Center for Cognitive Neuroscience, Center for the Study of Aging & Human Development, Durham Veterans Administration Medical Center, Navy Air Warfare Center Training Systems Division, and the Southeastern Federal Laboratory Consortium. The R&D program merges engineering and clinical aspects of ICT/AT technology design with outcomes research supported by federal, state, industry and philanthropic/development grants.
Kevin Caves, ME ATP RET, of the Physical Measures Core and his research colleagues, including Leighanne Jarvis, Sarah Moninger, Juliessa Pavon, MD, and Chandra (Sandy) Throckmorton, PhD, are defining and refining new technologies to assist in real-time assessment of physical activity. Here are samples of their research:

The Ecological Momentary Assessment (EMA) App utilizes an accelerometer in a watch to monitor individual activity levels in people who have difficulty delivering accurate self-reports to their clinical team. Individuals may also respond to personalized activity surveys on their watch or smartphone to record type and time of activity.

[Ecological Momentary Assessment (EMA) App Poster]

Gait or walking speed is a strong predictor of functional status and survival among older adults, but traditional measurement techniques may be prone to error between timers and trials. The Duke team is working to develop a low cost device that uses a LIDAR (Light Detection and Ranging) sensor and micro-controller to measure and display walking speed.

[Walking Speed Monitor Poster]

Movement and posture have been found to be predictors of hospital re-admission. Researchers are fine-tuning algorithms used to classify posture, with particular attention to elderly populations.
Sensor feedback may also be relayed to patients at risk of falling, e.g. Parkinson’s Disease patients, to improve posture. A trial study is planned for this population.

[Body Position Tracker poster]

[Accelerometer-based Positional Classification System poster]