TransKinect: a computer vision and machine learning clinical decision support system for automatic independent wheelchair transfer technique assessment

Abstract

Background

Physical and occupational therapists provide routine care for manual wheelchair users and are responsible for training and assessing the quality of transfers. These transfers can produce large loads on the upper extremity joints if improper sitting-pivot-technique is used. Methods to assess quality of transfers include the Transfer Assessment Instrument, a clinically validated tool derived from quantitative biomechanical features; however, adoption of this tool is low due to the complex usage requirements and speed of typical transfers.

Objective

The objective of this study is to develop and validate a computer vison and machine learning solution to better implement the Transfer Assessment Instrument in clinical settings.

Methods

The prototype system, TransKinect, consists of an infrared depth sensor and a custom software application; usability testing was carried out with fifteen therapists who performed two transfer assessments with the TransKinect. Proficiency in using features, usability, acceptability and satisfaction were analysed with validated surveys and themes were extracted from the qualitative feedback.

Results

The therapists were able to successfully complete the transfer quality assessments with 86.7 ± 5.4% proficiency. Total scores for System Usability Scale (77.6 ± 14.7%) and Questionnaire for User Interface Satisfaction (83.5 ± 8.7%) indicated that the system was usable and satisfactory. Qualitative feedback indicated that TransKinect was user-friendly, easy to learn, and had high potential.

Discussion

The results support TransKinect as a potential clinical decision support system for therapists for the comprehensive assessment of independent transfer technique. Future research is needed to investigate the utility and acceptance of TransKinect in real clinical environments.

Implications for Rehabilitation

• Machine learning and computer vision can be used to analyze transfer technique

• TransKinect is a usable and user-friendly means for therapists to automate analysis

• Summary reports and videos of transfers show high potential for clinical use

• Adoption of TransKinect can increase quality of care for manual wheelchair users

Graphical Abstract

Keywords:

• Biomechanics
• spinal cord injury
• human movement
• depth sensor
• marker-less motion capture

Acknowledgements

The authors would like to identify significant contribution from: Hailee Kulich PhD, Allison Brunswick, Jordan Witt, Jerimiah Johnson, and Alyssa Schnorenberg, PhD.

Ethics

The methodology and data presented in this manuscript were reviewed by the University of Pittsburgh Internal Review Board and the Department of Veterans Affairs Internal Review Board. There were not ethical issues to be addressed.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The contents of this paper do not represent the views of the Department of Veterans Affairs or the United States Government. This research was supported by the Human Engineering Research Laboratories of the University of Pittsburgh and the US Department of Veterans Affairs. Funding for this work was supported by VA Rehabilitation R&D Services Project #A2794-R and Project #F1580-P.