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Accuracy of video-based hand tracking for people with upper-body disabilities. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Volume 32, Pgs. 1863-1872.
NARIC Accession Number: J94081.
What's this?ISSN:1534-4320.
Author(s): Portnova-Fahreeva, Alexandra A.,
Yamagami, Momona,
Robert-Gonzalez, Adrià,
Mankoff, Jennifer,
Feldner, Heather,
Steele, Katherine M..
Project Number: 90ARCP0005.
Publication Year: 2024.
Number of Pages: 10.
Abstract: This study assessed the accuracy of Leap’s hand-tracking feature for individuals with and without upper-body disabilities for common dynamic tasks used in rehabilitation and evaluated a new technique to assess high-dimensional hand-tracking data via dimensionality reduction. Ten disabled and seven nondisabled participants were evaluated using Leap and an Optitrack motion capture system during tasks that were reflective of those commonly performed in clinical rehabilitation. The accuracy of Leap was compared with Optitrack using signal correlations, mean absolute errors, and digit segment length estimation. Researchers also evaluated the use of a dimensionality reduction technique, principal component analysis (PCA), to capture the complex, high-dimensional signal spaces of the hand. Leap’s hand-tracking performance did not differ between individuals with and without disabilities, yielding average signal correlations between 0.7 to 0.9. Both low and high mean absolute errors (between 10 to 80 mm) were observed across participants. Overall, Leap did well with general hand posture tracking, with the largest errors associated with the tracking of the index finger. Leap’s hand model was found to be most inaccurate in the proximal digit segment, underestimating digit lengths with errors as high as 18 mm. Using PCA to quantify differences between the high-dimensional spaces of Leap and motion capture showed that high correlations between latent space projections were associated with high accuracy in the original signal space. These results point to the potential of low dimensional representations of complex hand movements to support hand rehabilitation and assessment.
Descriptor Terms: AUDIOVISUAL MATERIALS, DEVICES EVALUATION, DEXTERITY, LIMBS, MEASUREMENTS, MOBILITY IMPAIRMENTS, MOTOR SKILLS, PERFORMANCE STANDARDS, REHABILITATION TECHNOLOGY, TASK ANALYSIS.
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Citation: Portnova-Fahreeva, Alexandra A., Yamagami, Momona, Robert-Gonzalez, Adrià, Mankoff, Jennifer, Feldner, Heather, Steele, Katherine M.. (2024.)
Accuracy of video-based hand tracking for people with upper-body disabilities. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, Pgs. 1863-1872. Retrieved 5/10/2026, from REHABDATA database.
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More information about this publication: IEEE Transactions on Neural Systems and Rehabilitation Engineering.