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. 2026 Aug 18;28:e91756. doi: 10.2196/91756

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©Md Saiful Islam, Sooyong Park, Evelyn Xiaoxiao Ma, Tariq Adnan, Ehsan Hoque. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.08.2026.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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SHAP waterfall plot for a correctly classified finger-tapping recording. The model correctly predicted the recording as poor, with extended idle time, long video duration, short tapping duration, and a smaller number of taps (than prescribed) emerging as the strongest contributors. Other features, such as brightness ratio and wrist visibility, had minor effects. The explanation highlights specific, actionable issues that users could correct by rerecording (eg, reducing idle time and completing more taps). SHAP: Shapley additive explanations.