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Smart Home Devices Can Reveal Behaviors Associated With Dementia

A machine learning algorithm applied to data from smart home devices could detect behavioral differences between healthy individuals and people with dementia

2 min read
An elderly lady using smart devices in her home.
Photo: iStockphoto

As people age, cognitive decline can happen in subtle ways that are not always immediately obvious to family members or friends. One solution for better detecting these subtle changes, however, could already be in the homes of many people, in the form of a smart home device.

In a recent study, researchers demonstrate that it’s possible to use data from smart home devices to detect behavioral differences between people who are experiencing cognitive decline and healthy individuals. The results, which could have broader implications for the monitoring of many different health conditions, were published 3 June in IEEE Journal of Biomedical and Health Informatics.

Gina Sprint, an Assistant Professor of Computer Science at Gonzaga University, is one of the researchers involved in the study. Sprint and her collaborators at Washington State University developed a novel algorithm for analyzing data from smart home devices; it’s called Behavior Change Detection for Groups (BCD-G). In particular, the algorithm analyzes behavioral patterns of residents across time.

In the study, 14 volunteers were monitored continuously in their homes for one month. Seven of these volunteers were living with dementia, while the other seven comprised a healthy control group of similar age and educational background. BCD-G was then used to assess the volunteers as they engaged in 16 types of activities, such as bathing, cooking, sleeping, working, and taking medications.

Using BCD-G to compare the two groups revealed some intriguing differences in behavior.

“First, the in-home walk speed of the cognitively impaired group was about half as fast as the age-matched healthy control group,” says Sprint. “Also, the cognitively impaired group had a greater variance in the duration of the activities they performed and what time they started the activities. They slept more during the day and at night, and lastly, they exhibited large behavioral differences related to how often and when they would leave their home, take their medications, and get dressed.”

While BCD-G proved useful for uncovering the behavioral patterns of people with dementia in this study, Sprint notes that the algorithm can be applied to a number of other health conditions. For example, BCD-G could be used to monitor patients recovering from a stroke or traumatic brain injury.

“Because BCD-G looks at changes across time points, it has potential to help with almost any condition where a clinician would want to know if someone is improving or declining,” explains Sprint.

Moving forward, her team plans to consult with clinicians to gather their feedback on BCD-G and further expand upon the tool. “Involving clinicians with frontline experience when creating algorithms like BCD-G is key to making machine learning applicable in the real-world,” she says. “Successful applications can assist clinicians in treatment planning and ultimately improve patients’ health.”

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This CAD Program Can Design New Organisms

Genetic engineers have a powerful new tool to write and edit DNA code

11 min read
A photo showing machinery in a lab

Foundries such as the Edinburgh Genome Foundry assemble fragments of synthetic DNA and send them to labs for testing in cells.

Edinburgh Genome Foundry, University of Edinburgh

In the next decade, medical science may finally advance cures for some of the most complex diseases that plague humanity. Many diseases are caused by mutations in the human genome, which can either be inherited from our parents (such as in cystic fibrosis), or acquired during life, such as most types of cancer. For some of these conditions, medical researchers have identified the exact mutations that lead to disease; but in many more, they're still seeking answers. And without understanding the cause of a problem, it's pretty tough to find a cure.

We believe that a key enabling technology in this quest is a computer-aided design (CAD) program for genome editing, which our organization is launching this week at the Genome Project-write (GP-write) conference.

With this CAD program, medical researchers will be able to quickly design hundreds of different genomes with any combination of mutations and send the genetic code to a company that manufactures strings of DNA. Those fragments of synthesized DNA can then be sent to a foundry for assembly, and finally to a lab where the designed genomes can be tested in cells. Based on how the cells grow, researchers can use the CAD program to iterate with a new batch of redesigned genomes, sharing data for collaborative efforts. Enabling fast redesign of thousands of variants can only be achieved through automation; at that scale, researchers just might identify the combinations of mutations that are causing genetic diseases. This is the first critical R&D step toward finding cures.

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