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AI-Powered Fall Risk Prediction in Nursing Care

NCT07000981 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 177

Last updated 2025-12-02

Study results available
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Summary

The goal of this study is to develop a nursing clinical decision support system for fall risk prediction using machine learning and computer vision techniques. The system is intended to offer advantages over traditional scales, including real-time analysis, contactless monitoring, objective evaluation, and personalized risk prediction-ultimately aiming to improve patient safety and reduce complications related to falls in clinical settings.

This study aims to answer the following questions:

Can machine learning models serve as valid tools for fall risk prediction?

Is the proposed system feasible for use in clinical environments?

Inclusion criteria for participants:

* Aged 18 years or older
* Able to read and write in Turkish
* Able to walk with or without assistance
* Willing to voluntarily participate in the study

Exclusion criteria:

* Inability to speak or understand Turkish adequately
* Being intubated
* Being physically restrained
* Being immobile
* Having a diagnosed cognitive impairment

Participants' basic information-including age, height, and weight-will be collected through a demographic data form. Fall risk will be initially assessed using the Morse Fall Scale. Then, a walking assessment will be conducted using a digital camera-based computer vision system as participants walk at a comfortable pace in a clinical corridor. Additionally, an accelerometer placed in the participants' pockets will record three-axis acceleration (X, Y, Z) during walking.

The data obtained will be analyzed using machine learning algorithms to estimate lower and upper limb biomechanics in real time. Features such as step length, cadence, gait cycle, and range of motion (ROM) will be extracted. These features, combined with Morse Fall Scale scores, will be used to train and validate an artificial neural network (ANN).

The study aims to contribute to the development of a reliable, objective, and real-time system capable of predicting fall risk in clinical environments through gait analysis.

Conditions

  • Accidental Falls
  • Risk Assessment
  • Nursing Assessment
  • Machine Learning
  • Computer Vision

Sponsors & Collaborators

  • Inonu University

    lead OTHER

Principal Investigators

  • Gürkan Özden, Assistant Professor · Inonu University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-01
Primary Completion
2025-07-01
Completion
2025-07-24

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07000981 on ClinicalTrials.gov