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TransKinect: a computer vision and machine learning clinical decision support system for automatic independent
Ahlad Neti1,2, Cheng-Shiu Chung1,3, Nithin Ayiluri1,3
1Human Engineering Research Laboratories, VA Pittsburgh HealthCare System, Pittsburgh, PA, USA.
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 RehabilitationMachine learning and computer vision can be used to analyze transfer techniqueTransKinect is a usable and user-friendly means for therapists to automate analysisSummary reports and videos of transfers show high potential for clinical useAdoption of TransKinect can increase quality of care for manual wheelchair users.
