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AI camera system could predict patient falls before they happen

NCT ID NCT07000981

Knowledge-focused Sponsor: Inonu University Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study tested a new system that uses cameras and artificial intelligence to predict fall risk in hospital patients. 177 adults walked in a corridor while a camera and a phone accelerometer recorded their movements. The system analyzed their gait to estimate fall risk, aiming to be more objective than traditional methods. The goal is to improve patient safety by identifying those at risk in real time.

What this could mean

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

What this could lead to
If successful, this system could provide a more objective and real-time way to predict fall risk in hospitals, potentially reducing fall-related injuries.
What could go wrong
This is a small, completed development study, not a large clinical trial. The system's accuracy and real-world usefulness need further testing in diverse settings before it can be widely adopted.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

177 people

The number who actually took part.

Started

Jun 2025

Finished

Jul 2025

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

The study is planned to be conducted on patients who meet the inclusion criteria and are hospitalized in the Physical Medicine and Rehabilitation department of a research hospital located in the eastern region of Turkey.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * 18 years of age or older * Accepting voluntary participation in the study * Be able to read and write Turkish * Being able to walk with or without support Exclusion Criteria: * Not being able to speak or understand Turkish adequately * Being intubated. * To be identified. * Being immobile * Having a mental disability.

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As listed by the trial registrant

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

Accidental falls Computer vision Machine learning Nursing assessment Risk assessment

Contacts and locations

Locations

  • Turgut Özal Medical Center

    Malatya, Battalgazi, 44280, Turkey (Türkiye)

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