A team at the University of Missouri is adapting in-home sensor technology to monitor people with amyotrophic lateral sclerosis (ALS). Bill Janes leads the effort to change systems originally developed by Marjorie Skubic and Marilyn Rantz for older adults. The sensors can detect changes in walking, sleeping and other daily activities.
Sensor signals travel wirelessly from the home through two small boxes and then transfer securely to university systems. Researchers use machine learning to build models that estimate a patient's score on the ALS Functional Rating Scale Revised (ALSFRS-R), which measures abilities like walking, talking, swallowing and breathing.
Noah Marchal leads the data science work and says the goal is to detect problems in advance, such as changes in gait or breathing that could lead to a fall or hospitalization. Later, the team plans to integrate alerts into clinical work so clinicians can check in, adjust medication, or suggest assistive devices.
Difficult words
- sensors — a small device that measures activity at home
- detect — to notice or discover a change or problem
- wirelessly — without using physical wires or cables
- machine learning — computer methods that learn from data
- models — a computer pattern that shows relationships
- estimate — to calculate or give an approximate number
- gait — the way a person walks
- hospitalization — the process of staying in hospital for care
- clinicians — a health professional who cares for patients
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- Do you think in-home sensors could help detect problems for other illnesses? Why or why not?
- What concerns might people have about signals that transfer to university systems?
- How would you feel if clinicians could check in after an alert from your home sensors?
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