The Reliability of a Video Analysis System (PnO Clinical Movement Data) and the Universal Goniometer in the Measurement of Hip, Knee, and Ankle Sagittal Plane Motion among Healthy Subjects

Mohsin, Fatma; McGarry, Anthony; Bowers, Robert

doi:10.1097/jpo.0000000000000201

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Manual measures also include inherent variation and error, which could be caused by inexact placement of a goniometer or tape measure. 18 For joint angle measures, Reissner et al 19 have shown that this error can be up to 7°with respect to the actual angle. It should be noted that computer vision and clinician estimates were taken from separate trials, so intertrial variability (and clinician error) may have affected our results.…”

Section: Discussionmentioning

confidence: 99%

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Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects

et al. 2023

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Objective Advances in computer vision make it possible to combine low-cost cameras with algorithms, enabling biomechanical measures of body function and rehabilitation programs to be performed anywhere. We evaluated a computer vision system's accuracy and concurrent validity for estimating clinically relevant biomechanical measures. Design Cross-sectional study. Setting Laboratory. Participants Thirty-one healthy participants and 31 patients with axial spondyloarthropathy. Intervention A series of clinical functional tests (including the gold standard Bath Ankylosing Spondylitis Metrology Index tests). Each test was performed twice: the first performance was recorded with a camera, and a computer vision algorithm was used to estimate variables. During the second performance, a clinician measured the same variables manually. Main measures Joint angles and inter-limb distances. Clinician measures were compared with computer vision estimates. Results For all tests, clinician and computer vision estimates were correlated ( r2 values: 0.360–0.768). There were no significant mean differences between methods for shoulder flexion (left: 2 ± 14° (mean ± standard deviation), t = 0.99, p < 0.33; right: 3 ± 15°, t = 1.57, p < 0.12), side flexion (left: − 0.5 ± 3.1 cm, t = −1.34, p = 0.19; right: 0.5 ± 3.4 cm, t = 1.05, p = 0.30) and lumbar flexion ( − 1.1 ± 8.2 cm, t = −1.05, p = 0.30). For all other movements, significant differences were observed, but could be corrected using a systematic offset. Conclusion We present a computer vision approach that estimates distances and angles from clinical movements recorded with a phone or webcam. In the future, this approach could be used to monitor functional capacity and support physical therapy management remotely.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects

et al. 2023

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“…18 Some variation in a goniometric joint angle measurement can emerge, if the physiotherapist has improper placement of its fulcrum over the centre of rotation of the joint or wrong anatomic structures. 19 Melián-Ortiz et al 20 have shown, that the variation can be 1° to 7° from the actual degree in manual joint measurements with a universal goniometer. To minimize the variation, physiotherapists should follow standard written principals.…”

Section: The Requirements On Computer Vision-based Motion Analysis From a Physiotherapist Point Of Viewmentioning

confidence: 96%

The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

Hellstén

Karlsson

Shamsuzzaman

et al. 2021

Rehabilitation Process and Outcome

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Background: Several factors, including the aging population and the recent corona pandemic, have increased the need for cost effective, easy-to-use and reliable telerehabilitation services. Computer vision-based marker-less human pose estimation is a promising variant of telerehabilitation and is currently an intensive research topic. It has attracted significant interest for detailed motion analysis, as it does not need arrangement of external fiducials while capturing motion data from images. This is promising for rehabilitation applications, as they enable analysis and supervision of clients’ exercises and reduce clients’ need for visiting physiotherapists in person. However, development of a marker-less motion analysis system with precise accuracy for joint identification, joint angle measurements and advanced motion analysis is an open challenge. Objectives: The main objective of this paper is to provide a critical overview of recent computer vision-based marker-less human pose estimation systems and their applicability for rehabilitation application. An overview of some existing marker-less rehabilitation applications is also provided. Methods: This paper presents a critical review of recent computer vision-based marker-less human pose estimation systems with focus on their provided joint localization accuracy in comparison to physiotherapy requirements and ease of use. The accuracy, in terms of the capability to measure the knee angle, is analysed using simulation. Results: Current pose estimation systems use 2D, 3D, multiple and single view-based techniques. The most promising techniques from a physiotherapy point of view are 3D marker-less pose estimation based on a single view as these can perform advanced motion analysis of the human body while only requiring a single camera and a computing device. Preliminary simulations reveal that some proposed systems already provide a sufficient accuracy for 2D joint angle estimations. Conclusions: Even though test results of different applications for some proposed techniques are promising, more rigour testing is required for validating their accuracy before they can be widely adopted in advanced rehabilitation applications.

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IoT enabled knee ROM measurement for FEST

Praveen,

Jasmine,

Dhanagopal

et al. 2024

AIP Conference Proceedings

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The Reliability of a Video Analysis System (PnO Clinical Movement Data) and the Universal Goniometer in the Measurement of Hip, Knee, and Ankle Sagittal Plane Motion among Healthy Subjects

Cited by 4 publications

References 14 publications

Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects

Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects

The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

IoT enabled knee ROM measurement for FEST

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