Wearable technology to help detect cognitive decline

Early detection is critical for slowing the decline of cognitive function and lowering the risk of Alzheimer’s and other dementias. As wearable technology becomes more widespread, it has become a useful tool for tracking various health statistics that could help pick up on deviation or decline in an individual’s typical performance. However, elderly populations, who could greatly benefit from this type of real-time continuous monitoring, have not widely adopted wearable technologies. An interdisciplinary Tufts team – from the School of Engineering, the Jean Mayer USDA Human Nutrition Research Center on Aging (HNRCA), and the Friedman School of Nutrition – is creating a wearable solution for detecting declines in cognitive and motor function that specifically caters to elderly populations.  

Current methods that monitor cognitive decline involve being assessed at a doctor’s office. While helpful, these evaluations can only capture a moment in time and, depending on the socio-economic status of the individual, may not be readily available in the first place. Moreover, difficulties with mobility and cognition may slowly mount over time as a person ages, meaning that decline can easily fly under the radar with sporadic assessments.

Motivated by this challenge, this $1.7 million dollar project recently funded by the National Institutes of Health (NIH) National Institute on Aging seeks to develop a low cost, user-friendly system capable of providing the required information from data collected by individuals going about their daily lives.  Working to achieve this goal is a diverse team of Tufts’ skilled faculty including Professor Sameer Sonkusale of the Department of Electrical and Computer Engineering, Professor and Dean of Research Matthew Panzer of the Department of Chemical and Biological Engineering, Professor Eric Miller of the Department of Electrical and Computer Engineering and HNRCA, and Professors José M. Ordovás and Kyla Shea, both of the HNRCA and Friedman School of Nutrition.

The project has two primary components– first, the team will design wearable technology that can monitor an array of biosignals, and second, they will develop algorithms that will use the sensor data to provide analysis and estimations of cognitive and mobility decline over time for the wearer. Using deep eutectic solvents, the group will create soft, wearable gel patches that are designed with elderly populations in mind. The clear patches will be designed to be as comfortable and unobtrusive as possible.

Sonkusale and Panzer will work together to create the sensors with their respective research in flexible bioelectronics and materials science. Bringing his expertise in machine learning and artificial intelligence, Miller will turn the raw sensor data into useful information. Ordovás and Shea will lead the human subject work to make sure the product is as effective as possible for the target population.

The patches will simultaneously monitor eight different domains, including gait, posture, head motion, heart rate variability, respiration, location, orientation, and movement. This comprehensive approach allows for the detection of possible cognitive or motor decline in any one of these key areas. Individuals could use these patches to monitor themselves in free-living environments, and would not need to be in a nursing home or other medical facility to receive round-the-clock monitoring.

The project is still in its early stages, but the group feels hopeful about the benefits that this could provide to users. Ultimately the team’s goal is to support older adults with healthy aging and prevention of Alzheimer’s and other dementias.