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SHB: Small: Enabling Technologies for Assessing and Assisting Independent Living

SHB: Small: Enabling Technologies for Assessing and Assisting Independent Living
SHB:小型:评估和协助独立生活的支持技术
批准号:
1118017
负责人:
Huaping Liu
金额:
$49.98万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2015-07-31

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中文摘要
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英文摘要
This research explores the development of a wireless in-home location monitoring system for assessing and assisting independent living of the elderly, whose population demographics in the U.S. alone will exceed 80 million by 2040. Key areas where in-home location-tracking technologies exhibit particularly significant potential for helping assess and maintain independence include medication management, motor changes (e.g. walking speed changes), and fall detection. Research has revealed that high-resolution localization information is key to precisely characterizing these activities. Existing localization systems are inadequate for this purpose due to their lack of multi-person tracking capability, poor reliability, limited resolution in space and time, and large form factors that make them uncomfortable for seniors to wear.The investigators focus on developing a robust, high-precision, 3-dimensional wireless localization system to simultaneously track multiple patients' position/movement and activities. Specifically, they develop ultra-low power, tiny transmitters that are unobtrusive for seniors to wear, and address several unsolved technical challenges such as wireless synchronization of distributed receivers and localization error due to multipath overlap. Inference algorithms utilizing the location data acquired by the system to effectively characterize medication taking are created. To validate the accuracy of the inference models within independent living settings, the system is deployed in both a controlled smart home environment and in the homes of the ORCATECH Living Lab community-dwelling seniors. While the focus is on medication management due to project scope/feasibility, the core technology created will enable characterization of various behaviors, including motor changes that are known to be predictive of patients' neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases.
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