AI and Home Sensors Transform ALS Care Monitoring

At the University of Missouri, researchers are pioneering a system that combines in‑home sensors with artificial intelligence to improve care for people living with amyotrophic lateral sclerosis (ALS). The technology is designed to capture subtle daily changes in health that often go unnoticed between clinic visits, giving patients and caregivers earlier warnings and more timely interventions.

The sensors, originally developed to track older adults, are being adapted for ALS to monitor walking, sleeping, and breathing patterns. Data flows securely to university servers, where machine learning models estimate scores on the ALS Functional Rating Scale Revised, the standard tool for measuring disease progression. This allows clinicians to see how patients are doing in real time rather than relying solely on periodic check‑ups.

The project leaders emphasize that the system can predict declines before they result in falls, hospitalizations, or other emergencies. Families involved in early testing report greater peace of mind, knowing that changes in mobility or respiratory function are detected quickly. Clinicians envision secure portals where they can review daily health trends and adjust treatment plans accordingly.

The study demonstrates how sensor‑based monitoring could transform ALS care. By providing continuous data, the system reduces the risk of sudden health crises and supports personalized treatment. It also highlights the potential for remote monitoring to ease the burden on patients who struggle to travel to clinics.

Beyond ALS, the researchers see applications for other chronic conditions such as Parkinson’s disease, multiple sclerosis, and heart failure. The same sensor and AI framework could be adapted to track disease‑specific markers, offering a scalable model for smarter home‑based care.