Abstract
BackgroundPerforming physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic.ObjectiveThis study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference.MethodsMovement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference.ResultsBoth algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details.ConclusionsThe chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.
Highlights
BackgroundRehabilitation is essential to regain lost or weakened functionality after injury or surgery
They are independent of the hardware being used and can be adapted for any motion-sensing device capable of tracking human skeleton joints, such as those mentioned in the Related Work section
The idea of worst performance was the key to converting the distance measures and likelihood values to similarity scores between 0% and 100%
Summary
Rehabilitation is essential to regain lost or weakened functionality after injury or surgery It is commonly initiated in a clinic and supervised by a physiotherapist, the prescribed therapeutic exercises will normally need to be practiced at home by patients on their own. Advances in virtual reality technologies have resulted in various virtual rehabilitation platforms introduced to address this issue [1,2,3] They are generally equipped with sensory devices to track and monitor the patient’s movement when performing the exercises. The scores enable the patient to monitor their daily performance They can be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed
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