Passive Activity Monitoring with Patient Identification and Gesture Detection

Award Information
Agency: Department of Health and Human Services
Branch: N/A
Contract: 1R43AG044167-01
Agency Tracking Number: R43AG044167
Amount: $152,845.00
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solicitation Year: 2012
Solicitation Topic Code: NIA
Solicitation Number: OD12-003
Small Business Information
6870 W. 52ND AVENUE, SUITE 110, DENVER, CO, 80002-
DUNS: 827078440
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 (303) 989-3424
Business Contact
Phone: (303) 250-1792
Research Institution
DESCRIPTION (provided by applicant): Elderly patients with Alzheimer's disease and dementia present a massive care challenge for family members and care professionals. In the last year of a patient's life, half of family caregivers report spending 46 or more hours a week assisting him/her with activities of daily living (ADL). Ingenium Care proposes to create a passive, self-learning, system for activity monitoring and support of elderly persons using the novel technology of Microsoft's Kinect device. Thisadvanced activity monitoring combined with Ingenium Care's interactive communications and support system will extend independent living and the successful conduct of everyday tasks for the elderly with Alzheimer's disease, dementia, people with disabilities, and soldiers with PTSD and TBI. Existing monitoring technology has limited activity recognition capability or uses many sensors and wearable devices. The need for wearable devices requires cooperation from the elderly that may not reminder or resent towear it. We propose to replace our existing wearable badge technology with a passive device based on the Kinect device from Microsoft. A network of these sensors provides precise location information within a home or facility and detects falls and gesturessuch as eating, drinking or taking medications. This device will eliminate the need to wear any device. Aim #1 - Goal: Develop Algorithms for Proof of Concept Gesture Recognition. Aim #2 - Goal: Do laboratory training and testing of detection algorithms utilizing a single work station. Aim #3 - Goal: Perform randomized activities and gestures in a simulated living environment and iteratively improve algorithms. PUBLIC HEALTH RELEVANCE: Narrative Today, 60 million Americans - one in five - requireassistance in their living arrangements and daily activities. These are primarily elderly individuals with Alzheimer's disease, dementia, and also persons with disabilities. By applying the latest monitoring and artificial intelligence technologies, the outcomes of this research would enable the Ingenium Care system to improve the quality of home and institutional health care, and at the same time, reduce the cost of providing that care. The marketplace for technology to assist the elderly will grow sharply from 2 billion today to more than 20 billion by 2020, according to new reports from Frost and Sullivan and Forester Research (Liz Boehm, Principal Analyst for Healthcare and Life Sciences) entitled Healthcare Unbound's Early Self-Pay Market .

* information listed above is at the time of submission.

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