As people age they begin to need assistance with activities of daily life (ADLs). Losing the ability to perform ADLs requires that these individuals receive care from family members, move to senior living communities or experience a significant drop in their quality of life. Given the projected rise in the number of older adults over the coming decades, the resources available to care for this population will be spread thin, potentially leaving some with lower quality of care or even without any form of care. One means of addressing this is developing systems that allow older adults to live independently while maintaining their quality of life for longer. Such systems would not only meet a well-documented desire to live independently, but also reduce the burden of care on caregivers and/or the need for more expensive forms of long-term care. This project will lay the groundwork for the development of a scalable gerontechnological system consisting of sensors and novel algorithms designed to recognize ADL performance in real-world settings. Through tracking of resident performance of ADLs we aim to facilitate more timely and relevant care and interventions.

The project works with multiple senior living communities to develop a gerontechnological system that allows for a quantified daily health profile to enable meaningful care for those who wish to age-in-place. This planning grant attempts to answer the following research questions through interviews and observational studies with each collaborating senior living community and determination of preliminary technological details: (1) What are the specific needs of both caregivers and residents from a gerontechnological system built on the recognition of ADLs and resident location? (2) What are the requirements of the indoor positioning system (IPS)? (3) What are the privacy needs and concerns of the caretakers and residents and how can the system address them? The information gained from the planning grant will allow us to submit a multi-year Integrative Research Grant that will attempt to answer the following research questions: (1) Can common ADLs be accurately recognized using a combination of wearable devices and novel machine learning algorithms and methodologies? (2) How does the recognition of activity patterns, feedback to residents and caregivers, and individually-tailored interventions support and/or impact the lives of senior living community residents? (3) How does the recognition of activity patterns, feedback to residents and caregivers, and individually-tailored ADL support and/or impact the workload and ease the burden of care on the caregivers within the senior living facilities? Successful completion of this work has the potential to transform the current paradigm of elderly healthcare from reactive to proactive by monitoring and supporting the health of the elderly population.