Related Experiment Video
Updated: Jan 17, 2026
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
PilOT-Measure: a mobile 3D depth sensing application to support accurate and efficient clinician-led home-based falls
Zear Ibrahim1, Arthur G Money2, Anita Atwal3
1Department of Computer Science, Brunel University London, Uxbridge, UB8 3PH, UK.
Background:
An aging global population, coupled with high levels of assistive equipment abandonment, has propelled increases in falls-related injuries at home. Equipment abandonment occurs, in-part, due to inaccurate measurements of the patient's home taken during the falls risk assessment process. There is an urgent need to explore the value of new digital mobile technologies to help clinicians to take more efficient and effective measurements of patient's home, thereby enhancing the efficacy of falls risk assessments and potentially minimising equipment abandonment.
Aim:
The aim of this study is to present and evaluate the accuracy and efficiency of PilOT-Measure, a digital mobile 3D depth-sensor-enabled measurement guidance application for use by clinicians carrying out falls risk assessments.
Methods:
Twenty-one trainee and registered Occupational Therapists took part in this repeated-measures, mixed methods study to evaluate measurement accuracy, task completion time, and overall system usability and user perceptions of the application.
Results:
For measurement accuracy, PilOT-Measure outperformed current state of the art handheld tape measure and paper-based measurement guidance booklet. For accuracy consistency, the handheld tape measure and booklet was more consistently accurate for six out of 11 cases. However, PilOT-Measure tended to facilitate significantly faster task completion times, suggesting potential task efficiency benefits. In terms of usability, participants favoured PilOT-Measure and saw potential to reduce administrative tasks and support joint decision-making. Concerns about marker placement on reflective surfaces and patient privacy were noted.
Conclusions:
This study highlights the positive role that mobile depth-sensing technologies can potentially play in improving the efficiency and accuracy of falls risk assessments, hence, reducing levels of equipment abandonment and falls related injuries at home. Future work will focus on improving marker placement, measurement accuracy, and accuracy consistency and explore the potential of using PilOT-Measure as a falls risk patient self-assessment tool.
