Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke
Many medical conditions impair proprioception but there are few easy-to-deploy technologies for assessing proprioceptive deficits. Here, we developed a method—called “OpenPoint”—to quantify upper extremity (UE) proprioception using only a webcam as the sensor. OpenPoint automates a classic neurologi...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7434 |
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| author | Guillem Cornella-Barba Andria J. Farrens Christopher A. Johnson Luis Garcia-Fernandez Vicky Chan David J. Reinkensmeyer |
| author_facet | Guillem Cornella-Barba Andria J. Farrens Christopher A. Johnson Luis Garcia-Fernandez Vicky Chan David J. Reinkensmeyer |
| author_sort | Guillem Cornella-Barba |
| collection | DOAJ |
| description | Many medical conditions impair proprioception but there are few easy-to-deploy technologies for assessing proprioceptive deficits. Here, we developed a method—called “OpenPoint”—to quantify upper extremity (UE) proprioception using only a webcam as the sensor. OpenPoint automates a classic neurological test: the ability of a person to use one hand to point to a finger on their other hand with vision obscured. Proprioception ability is quantified with pointing error in the frontal plane measured by a deep-learning-based, computer vision library (MediaPipe). In a first experiment with 40 unimpaired adults, pointing error significantly increased when we replaced the target hand with a fake hand, verifying that this task depends on the availability of proprioceptive information from the target hand, and that we can reliably detect this dependence with computer vision. In a second experiment, we quantified UE proprioceptive ability in 16 post-stroke participants. Individuals post stroke exhibited increased pointing error (<i>p</i> < 0.001) that was correlated with finger proprioceptive error measured with an independent, robotic assessment (r = 0.62, <i>p</i> = 0.02). These results validate a novel method to assess UE proprioception ability using affordable computer technology, which provides a potential means to democratize quantitative proprioception testing in clinical and telemedicine environments. |
| format | Article |
| id | doaj-art-927f4553b5df4c1b8be20c4f24b70eb0 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-927f4553b5df4c1b8be20c4f24b70eb02025-08-20T01:55:38ZengMDPI AGSensors1424-82202024-11-012423743410.3390/s24237434Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post StrokeGuillem Cornella-Barba0Andria J. Farrens1Christopher A. Johnson2Luis Garcia-Fernandez3Vicky Chan4David J. Reinkensmeyer5Department of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA 92697, USADepartment of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA 92697, USARancho Los Amigos National Rehabilitation Center, Rancho Research Institute, Downey, CA 90242, USADepartment of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA 92697, USAIrvine Medical Center, Department of Rehabilitation Services, University of California, Orange, CA 92868, USADepartment of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA 92697, USAMany medical conditions impair proprioception but there are few easy-to-deploy technologies for assessing proprioceptive deficits. Here, we developed a method—called “OpenPoint”—to quantify upper extremity (UE) proprioception using only a webcam as the sensor. OpenPoint automates a classic neurological test: the ability of a person to use one hand to point to a finger on their other hand with vision obscured. Proprioception ability is quantified with pointing error in the frontal plane measured by a deep-learning-based, computer vision library (MediaPipe). In a first experiment with 40 unimpaired adults, pointing error significantly increased when we replaced the target hand with a fake hand, verifying that this task depends on the availability of proprioceptive information from the target hand, and that we can reliably detect this dependence with computer vision. In a second experiment, we quantified UE proprioceptive ability in 16 post-stroke participants. Individuals post stroke exhibited increased pointing error (<i>p</i> < 0.001) that was correlated with finger proprioceptive error measured with an independent, robotic assessment (r = 0.62, <i>p</i> = 0.02). These results validate a novel method to assess UE proprioception ability using affordable computer technology, which provides a potential means to democratize quantitative proprioception testing in clinical and telemedicine environments.https://www.mdpi.com/1424-8220/24/23/7434proprioceptioncomputer visionpointing errorhome-based rehabilitation |
| spellingShingle | Guillem Cornella-Barba Andria J. Farrens Christopher A. Johnson Luis Garcia-Fernandez Vicky Chan David J. Reinkensmeyer Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke Sensors proprioception computer vision pointing error home-based rehabilitation |
| title | Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke |
| title_full | Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke |
| title_fullStr | Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke |
| title_full_unstemmed | Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke |
| title_short | Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke |
| title_sort | using a webcam to assess upper extremity proprioception experimental validation and application to persons post stroke |
| topic | proprioception computer vision pointing error home-based rehabilitation |
| url | https://www.mdpi.com/1424-8220/24/23/7434 |
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