KSTC-184-512-15-224

Proposal Title HEAD-MOUNTED ACTIVITY TRACKING (HAM) SYSTEM FAST TRACK
Category Information Technology and Communications
Abstract Fall-related injuries among older adults, especially among older women, are associated with substantial annual economic costs (>$19 billion) and are borne by individuals, society, and the medical care system. Elderly in the long-term home care setting, especially those who are socially isolated, are at particularly high risk. In addition, social isolation contributes to a wide array of other physical and psychological health risks. The population of the United States, as in most developed nations, is aging and is doing so at an increasing rate. This change in demographics is having a profound effect on the nation's healthcare delivery system and the availability of resources to meet the needs of long-term home care for the elderly. Nonetheless, the strong predilection of most of the elderly, and their families, is to maintain the independence afforded by long-term care delivery in the home rather than in the institutionalized setting. Properly managed, home care can have a significantly positive effect on controlling or even reducing the cost of long-term elderly care. In order to deliver safe, high quality long-term elderly home care that meets resource and demographic limitations, an integrated, technology-based infrastructure optimized for this purpose will be required. We propose to accomplish this by developing an innovative wearable technology called the Head-mounted Activity Monitoring (HAM) System. This device will possess functions and a form-factor that will provide a complete monitoring solution that performs functions, such as fall prediction, that will enable targeted and timely interventions designed to prevent the fall. It will accomplish this through the integration of application-specific machine learning algorithms with MEMS sensors (accelerometer, gyro, magnetometer, GPS receiver) and other components that will enable activity monitoring and communication functions, thereby increasing overall home care safety and reduced social isolation.
Amount Awarded $500,000
Keywords wearable, computer, activity, tracking, machine, learning, head, mounted
Point of Contact Mark Fauci
6508475745
mfauci@gen9.com
Gen Nine, Inc
Location Louisville