Novel Clinical Applications of Marker-less Motion Capture as a Low-cost Human Motion Analysis Method in the Detection and Treatment of Knee Osteoarthritis

Armstrong, Kai, Wen, Yan, Zhang, Lei , Ye, Xujiong and Lee, Paul (2021) Novel Clinical Applications of Marker-less Motion Capture as a Low-cost Human Motion Analysis Method in the Detection and Treatment of Knee Osteoarthritis. Journal of Arthritis, 11 (1). pp. 1-5. ISSN 2167-7921

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Novel Clinical Applications of Marker-less Motion Capture as a Low-cost Human Motion Analysis Method in the Detection and Treatment of Knee Osteoarthritis
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Abstract

Marker-less motion capture is a rapidly advancing field that can take
simple RGB image sequences, or more advanced Red Green Blue Depth
(RGB-D) image sequences obtained using depth sensors, and outputs an
estimated human pose. This method of human pose estimation allows for
the extraction of biomechanical features which can then be analysed by
clinicians to give more insights into a patient’s movement capabilities.
When compared to other, more clinically proven technologies such as the
Knee Kinesiography (KneeKG), biomechanics presented have the advantage
of being more representative of natural movement without the obstructive
markers placed on the body. This Significant difference of up to 10 degrees
in a range of motion for the knee could be the key to better identifying a
person’s gait or tracking their natural walking pattern over time, while also
being more robust and better suited to a smaller clinical environment.

Keywords:Human motion analysis, artificial intelligence, Computer-Aided Diagnosis (CAD)
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:48031
Deposited On:09 Feb 2022 16:54

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