SBIR/STTR Research Award

Passive Activity Monitoring with Patient Identification and Gesture Detection

Award identifier d8861c745c4f3bf75c6306a4e44f08d899173c1be1d433aea9abb49dfb50143eAwarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$152,845

Program and phase

SBIR · Phase I

Award year

2012

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Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
INGENIUM CARE, LLC
Proposal award date
Not reported
Contract end date
Not reported
UEI
Not reported
Solicitation number
OD12-003
Topic code
NIA

Research abstract

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 .

Technology keywords

Not reported

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Agency intelligence

Department of Health and Human Services

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