Can smart sensors predict falls before they happen?

NCT ID NCT04368858

First seen Aug 05, 2026 · Last updated Aug 06, 2026 · Updated 1 time

Summary

This study explores whether wearable motion sensors can improve screening for fall risk in people with chronic conditions like osteoarthritis, Parkinson's disease, COPD, and obesity. Participants will perform two standard walking tests while wearing small sensors that measure movement and balance. The goal is to see if these sensor-based measurements can more precisely identify who is at risk of falling, potentially guiding better prevention strategies.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance
Wearable inertial sensors (mTUG® and mGAIT®) used during functional tests (Timed-Up and Go, 6-minute walk) to assess fall risk
What this could lead to
If successful, this could lead to a more precise, sensor-based screening tool to identify fall risk in chronic illness patients, enabling earlier and targeted interventions.
What could go wrong
This is an exploratory study, so the sensor measurements may not reliably predict falls. Results may not generalize beyond the specific chronic conditions studied.

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Conditions

The condition(s) this trial relates to.

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Contacts and locations

Locations

  • Chu Clermont Ferrand

    Clermont-Ferrand, 63003, France

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