SBIR Phase I: Developing the internet of livable spaces for older adults

Period of Performance: 07/01/2016 - 06/30/2017

$225K

Phase 1 SBIR

Recipient Firm

GAiTE LLC
460 Turner Street NW Suite 102
Blacksburg, VA 24060
Firm POC, Principal Investigator

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the improvement in the quality of life for older adults currently affected by falls. Every year over 700,000 older adults are hospitalized in the United States due to fall-related injuries, which result in $34B in direct medical costs. The proposed technology will reduce first and future falls by providing an older adult's medical treatment team (e.g., physician, physical therapist, etc.) with fall history and ambulatory information. Fall histories are difficult to obtain and unreliable (as many occurrences are not self-reported). The underlying technological development can be further expanded to allow smart buildings to behave as "first responders" and aid their occupants during man-made and natural disasters. Extensions of this technology have potential in many fields, including efficient energy management systems, security and threat detection, emergency response and evacuation, and structural health monitoring. The proposed technology respects privacy while enabling significant improvement in infrastructure intelligence. This Small Business Innovation Research (SBIR) Phase I project will develop a state-of-the-art lifestyle-monitoring system for older adults in assisted homes that has a comprehensive approach towards fall prevention and detection. The healthcare industry has invested significant resources in predicting falls based on the number of previous falls, but current healthcare professional's access to individual patient fall history is limited due to unreliable self-reporting. Additionally, research has shown that the risk of future falls can be greatly reduced through timely preventative treatments. This prevention is achieved by indirectly monitoring the physical activity and falls of older adults through floor-mounted accelerometers coupled with data analytics processing. This analysis studies inherent patterns in recorded data of events such as falls, walking, door slams, etc. Prediction is made possible through the recording of the unreported falls and health history. The anticipated output of this system is a means of detecting falls, locating them, and effective historical data storage, which will result in a more accurate future risk evaluation of the older adult.