Abstract
Introduction
Gait analysis systems have enjoyed increasing usage and have been validated to provide highly accurate assessments for range of motion. Size, cost, need for marker placement and need for complex data processing have remained limiting factors in uptake outside of what remains predominantly large research institutions. Progress and advances in deep neural networks, trained on millions of clinically labelled datasets, have allowed the development of a computer vision system which enables assessment using a handheld smartphone with no markers and accurate range of motion for knee during flexion and extension. This allows clinicians and therapists to objectively track progress without the need for complex and expensive equipment or time-consuming analysis, which was concluded to be lacking during a recent systematic review of existing applications.
Method
A smartphone based computer vision system was assessed for accuracy with a gold standard comparison using a validated ‘traditional’ infra-red motion capture system which had a defined calibrated accuracy of 0.1degrees. A total of 22 subjects were assessed simultaneously using both the computer vision smartphone application and the standard motion capture system. Assessment of the handheld system was made by comparison to the motion capture system for knee flexion and extension angles through a range of motion with a simulated fixed-flexion deformity which prevented full extension to assess the accuracy of the system, repeating movements ten times. The peak extension angles and also numerous discrete angle measurements were compared between the two systems. Repeatability was assessed by comparing several sequential cycles of flexion/extension and comparison of the maximum range of motion in normal knees and in those with a simulated fixed-flexion deformity. In addition, discrete angles were also measured on both legs of three cadavers with both skin and then bone implanted fiducial markers for ground truth reliability accounting for skin movement. Data was processed quickly through an automated secure cloud system.
Results
The smartphone application was found to be accurate to 1.47±1.05 degrees through a full range of motion and 1.75±1.56 degrees when only peak extension angles were compared, demonstrating excellent reliability and repeatability. The cadaveric studies despite limitations which will be discussed still showed excellent accuracy with average errors as low as 0.29 degrees for individual angles and 4.09 degrees for an average error in several measurement.
Conclusion
This novel solution offers for the first time a way to objectively measure knee range of motion using a markerless handheld device and enables tracking through a range of assessments with proven accuracy and reliability even accounting for traditional issues with the previous marker based systems. Repeatability for both computer vision and motion capture have greater extrinsic than intrinsic error, particularly with marker placement - another benefit of a markerless system.
Clinical applications include pre-operative assessment and post-operative follow-up, paired with surgical planning (including with robots) and remote monitoring after knee surgery, with outcomes guiding treatment and rehabilitation and leading to reduced need for manipulation under anaesthesia and greater satisfaction.
