Smartphone Vibrometric Force Estimation for Grip Related Strength Measurements
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909941505196032 |
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| author | Barry, Colin Wang, Edward Jay |
| author_facet | Barry, Colin Wang, Edward Jay |
| contents | Hand grip strength is a widely used clinical biomarker linked to mobility, frailty, surgical outcomes, and overall health. This work explores a novel, phone only approach for estimating grip related force using a smartphone's built in vibration motor and inertial measurement unit. When the phone vibrates, applied finger force modulates the amplitude of high frequency accelerometer and gyroscope signals through Vibrometric Force Estimation. We profiled a Google Pixel 4 using synchronized IMU data and ground truth force measurements across varied force trajectories, then trained ridge regression models for both absolute and relative force prediction. In 15 fold hold one out validation, absolute force estimation achieved a mean absolute error of 1.88 lbs, while relative force estimation achieved a mean error of 10.1%. Although the method captures pinch type force rather than standardized full hand HGS, the results demonstrate the feasibility of smartphone based strength assessment using only on device sensors. This approach may enable large scale, low burden functional health measurements once profiling is completed for major smartphone models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smartphone Vibrometric Force Estimation for Grip Related Strength Measurements Barry, Colin Wang, Edward Jay Human-Computer Interaction Hand grip strength is a widely used clinical biomarker linked to mobility, frailty, surgical outcomes, and overall health. This work explores a novel, phone only approach for estimating grip related force using a smartphone's built in vibration motor and inertial measurement unit. When the phone vibrates, applied finger force modulates the amplitude of high frequency accelerometer and gyroscope signals through Vibrometric Force Estimation. We profiled a Google Pixel 4 using synchronized IMU data and ground truth force measurements across varied force trajectories, then trained ridge regression models for both absolute and relative force prediction. In 15 fold hold one out validation, absolute force estimation achieved a mean absolute error of 1.88 lbs, while relative force estimation achieved a mean error of 10.1%. Although the method captures pinch type force rather than standardized full hand HGS, the results demonstrate the feasibility of smartphone based strength assessment using only on device sensors. This approach may enable large scale, low burden functional health measurements once profiling is completed for major smartphone models. |
| title | Smartphone Vibrometric Force Estimation for Grip Related Strength Measurements |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.03186 |