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Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation
Joe Sarsfield1, David Brown1, Nasser Sherkat2
1Interactive Systems Research Group, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, United Kingdom.
Consumer technology like the Xbox One Kinect can aid home rehabilitation. Analysis shows pose estimation needs improvement, particularly for upper-body stroke recovery joint tracking.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Computer Vision
Background:
- Home-based rehabilitation offers cost-effective solutions.
- Consumer-level technology presents opportunities for remote patient monitoring and therapy.
- Pose estimation algorithms are crucial for motion analysis in rehabilitation.
Purpose of the Study:
- To clinically analyze the pose estimation algorithms of the Xbox One Kinect.
- To assess the suitability of consumer-grade technology for supervised rehabilitation.
- To guide the development of future pose estimation algorithms for rehabilitation.
Main Methods:
- Qualitative and quantitative clinical analysis of pose estimation algorithms.
- Focus on upper-body stroke rehabilitation as a use case.
- Evaluation of joint tracking accuracy and occlusion handling.
Main Results:
- The Kinect's pose estimation algorithms require enhancements for joint tracking.
- Specific challenges were identified with shoulder, elbow, and wrist joint tracking.
- Improved tracking is needed during full or partial occlusion in depth data.
Conclusions:
- Consumer-grade pose estimation technology shows potential for home rehabilitation.
- Further algorithm development is necessary for reliable upper-body stroke rehabilitation.
- Exploiting temporal information can improve tracking accuracy in challenging conditions.
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