Development of Kinect-Based Proprioception Evaluation and Classification System

Proprioception is the fundamental sense that enables the central nervous system to process body positions, postures, and movements in functional tasks. Aging and neurological disorders often lead to proprioceptive impairments that affect balance and coordination. This study developed a contactless system using Kinect and Unity 3D to evaluate proprioception by quantifying joint angles under visual and positional restrictions. Participants performed specific postures with proprioceptive limitations induced by 2 kg weights and closed-eye conditions. Paired t tests revealed significant proprioceptive variations under these constraints. Random forest achieved a classification accuracy of 96%, effectively distinguishing sensory changes across conditions. These results highlight the potential of this system for rehabilitation and training programs targeting proprioceptive function.

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Publication Details

Journal
The Transactions of The Korean Institute of Electrical Engineers
Published
2025-08-07
DOI
https://doi.org/10.5370/kiee.2025.74.8.1373
Primary Topic
Innovation in Digital Healthcare Systems
Type
article
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article

Development of Kinect-Based Proprioception Evaluation and Classification System

Onseok Lee, Eunbin Kim, JaeMu Yun
The Transactions of The Korean Institute of Electrical Engineers
Innovation in Digital Healthcare Systems
article

Development of Kinect-Based Proprioception Evaluation and Classification System

Onseok Lee, Eunbin Kim, JaeMu Yun
article en

Abstract

Proprioception is the fundamental sense that enables the central nervous system to process body positions, postures, and movements in functional tasks. Aging and neurological disorders often lead to proprioceptive impairments that affect balance and coordination. This study developed a contactless system using Kinect and Unity 3D to evaluate proprioception by quantifying joint angles under visual and positional restrictions. Participants performed specific postures with proprioceptive limitations induced by 2 kg weights and closed-eye conditions. Paired t tests revealed significant proprioceptive variations under these constraints. Random forest achieved a classification accuracy of 96%, effectively distinguishing sensory changes across conditions. These results highlight the potential of this system for rehabilitation and training programs targeting proprioceptive function.

The Transactions of The Korean Institute of Electrical EngineersVol. 74(8)
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Openalex Percentile: Top 76%
Innovation in Digital Healthcare Systems
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