Deep learning is helping to protect elderly people from potentially catastrophic tumbles.
What’s happening: More than 2,000 senior living facilities across the U.S. use a diagnostic system called VirtuSense Balance to keep residents on their feet.
How it works: The system helps a specialist spot postures and motions that could contribute to a fall. It scans patients with infrared light as they perform a series of motions. A pose detection model analyzes their positions, a company spokesperson told The Batch.
- A balance test measures how much a person sways while standing still.
- A gait test assesses walking speed, the angles of the knees, and length of each step.
- In function tests, the system analyzes various sitting, standing, and walking activities.
- The system compares input from a given patient with norms for their age group, then assigns a fall risk score. It also provides to caregivers recommendations for improving the patient’s mobility.
Behind the news: Automated systems are helping to improve elder care in various ways.
- CarePredict is a wearable device that tracks patient behavior and alerts caregivers if they aren’t eating or sleeping well.
- The People Power Family system uses sensors to monitor seniors living at home for falls, late-night activity, and unexpected comings and goings. A model learns each patient’s habits and sends out warnings when they diverge in alarming ways.
Why it matters: Falls kill thousands of elderly adults each year and injure millions more. Highlighting risk factors could save lives, reduce insurance premiums, and help caregivers use their time more efficiently.
We’re thinking: AI has a clear role to play in caring for a surging elderly population. However, a recent study found that many older people resented and resisted being monitored by electronic systems. Technologists and health care practitioners alike must build such systems with compassion and respect for the people who will use them.