Catalyst Awardee

Project Description

Human Activity Recognition to Avoid Fall Related Injuries in the Older Adult Population

Rebecca Tarbert, DPT, Arun Jayaraman, PT, PhD, and Wamis Singhatat, MSBE

Competition Sponsor: US National Academy of Medicine

One out of four older adults experience a fall each year and falls are the leading cause of death from injury for older adults in the United States. The cost of falls average greater than $50 billion and continues to create ongoing cost for individuals and families due to subsequent disability. Fall management and mitigation strategies have focused on identification of fall risk factors and assessment provided by health care providers when accessible. The mystery of why falls occur is many times due to the unwitnessed events of falls and near-falls that occur leading up to a serious fall injury. The ability of a body worn sensor which accurately detects fall motions to provide fall analysis by which to offer specific interventions can provide answers to avoiding falls and fall injuries for the most vulnerable. Clinic visits allow only a sample of physical performance assessment during examination and cannot provide the medical professional with specific information regarding how the person reacts to perturbations in natural contexts. This research will be using a wearable smart belt containing a 3D sensor on the lower back area which continuously monitors human motion to provide activity recognition and insight for recognition and categorization of types of falls. What motions the individual encounters and how they physically respond within near-fall and fall-type scenarios can provide insight on where areas of intervention including strength, flexibility and balance training can be provided. This information regarding activities precipitating falls can offer medical insight to avoid serious fall injuries.

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