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Related Experiment Video

Updated: Apr 22, 2026

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

Published on: February 5, 2020

Data-driven framework for adverse manual wheelchair event risk classification and prevention.

Anand Mhatre¹, Muyun Zhao¹, Theresa Berner^1,2

¹Division of Occupational Therapy, School of Health and Rehabilitation Sciences, College of Medicine, The Ohio State University, Columbus, OH, USA.

Disability and Rehabilitation. Assistive Technology

|April 20, 2026

Summary

This study combined Failure Mode and Effects Analysis (FMEA) and natural language processing (NLP) to identify high-risk manual wheelchair components and scenarios, aiming to reduce user falls and injuries. Findings highlight casters, belts, brakes, and frames as critical components needing attention for improved wheelchair safety.

Keywords:

Adverse event failure mode and effect analysis natural Language processing risk assessment safety wheelchair

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Last Updated: Apr 22, 2026

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

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Published on: February 5, 2020

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Area of Science:

Biomedical Engineering
Rehabilitation Engineering
Human Factors Engineering

Background:

Manual wheelchairs are prone to component failures, leading to adverse incidents like falls and injuries.
Existing research lacks systematic frameworks to identify high-risk scenarios and root causes across diverse manual wheelchair models.

Purpose of the Study:

To develop a risk classification framework by integrating Failure Mode and Effects Analysis (FMEA) with natural language processing (NLP).
To propose guidelines for reducing risks associated with manual wheelchair use.

Main Methods:

Analysis of 752 adverse incidents reported by NHS WestMARC (2020-2025) across five manual wheelchair models.
Application of FMEA to calculate severity, occurrence, and criticality scores for wheelchair components.
Utilization of NLP to identify contextual risk factors and relative risk (RR) metrics for falls and injuries.

Main Results:

Tilt-in-space, ultralight, and paediatric models showed slightly elevated risks of falls/injuries post-failure (RR=1.08-1.29).
NLP identified transfer incidents, outdoor navigation, and belt misuse as key contextual factors.
High-risk components included casters, belts, brakes, frames, and the wheelchair system itself, based on FMEA criticality scores.

Conclusions:

The integrated FMEA-NLP framework effectively identifies high-risk manual wheelchair scenarios and components.
Findings support a tiered prevention strategy encompassing design improvements, user training, and regular servicing.
Integrating NLP and FMEA into incident reporting can enhance clinical practice, policy, and product design for greater wheelchair safety.