Classification of wheelchair pressure relief maneuvers using changes in center of pressure and weight on the seat

Abstract

Background

Pressure injuries from prolonged sitting are a significant problem for wheelchair users incurring high costs in healthcare expenditures and reducing quality-of-life. There is a need to improve pressure relief training and adherence in a variety of settings.

Objective

To identify effective common wheelchair pressure relief (PR) manoeuvres based on changes to users’ seated centre of pressure (CoP) and seated weight.

Participants

20 individuals who use manual wheelchairs as their primary means of mobility

Methods

Participants performed 5 types of PR including seated push-ups, leftward, rightward, forward, and backward leans—while sitting in a wheelchair equipped with a custom instrumented seat pan support. Data were analysed using both clustering and decision tree approaches to identify types of PR.

Results

Both clustering and decision tree approaches were able to identify and classify PR though neither could accurately distinguish between forward and backward PR.

Conclusion

Changes in the centre of pressure and the total weight on the wheelchair’s seat can be used to automatically characterise type, amplitude and duration of pressure relief manoeuvres. Building such a classification and quality assessment scheme into an algorithm could enable a virtual coaching system to track users’ pressure relief behaviour and make suggestions to improve adherence with clinical recommendations.

IMPLICATIONS FOR REHABILITATION

• Multiple bending beam load cells can be used to measure wheelchair users’ seated centre of pressure independent of type of cushion used.

• Both cluster analysis and decision tree algorithms can classify commonly practiced pressure reliefs by measuring changes to the centre of pressure and total weight on the wheelchair’s seat.

• The combination of force sensing for centre of pressure determination and either algorithm could serve as the basis for an application to coach wheelchair users to do effective pressure reliefs.

Keywords:

• Artificial intelligence
• machine learning
• pressure injury
• preventative medicine
• rehabilitation
• wheelchair

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

Authors are inventors on patent application No: 62/696,414, related to this technology; however, the technology is not currently under licence or option to licence.

Additional information

Funding

This work was supported by Craig H. Neilsen Foundation, DSF Charitable Foundation, Department of Defense Congressionally Directed Medical Research Programs (CDMRP), US National Science Foundation, Paralysed Veterans of America.