Accepted for/Published in: JMIR Human Factors
Date Submitted: Dec 25, 2025
Date Accepted: Feb 26, 2026
Estimating Grip Strength Using Input Data from a Commodity Smartphone
ABSTRACT
Background:
Grip strength is a crucial indicator for muscle deterioration, recovery, sarcopenia, and neurological disorders. However, conventional measurement requires a dedicated dynamometer, which limits accessibility and requires specific motions.
Objective:
To propose and validate a method for estimating grip strength using standard smartphone operations, eliminating the need for specialized equipment.
Methods:
Data were collected from 21 participants who performed standard smartphone tasks (tapping, flicking, and dragging) after measuring their grip strength with a dynamometer. A predictive regression model was developed using touch coordinates, timestamps, contact area, and inertial sensor data (acceleration, angular velocity, orientation).
Results:
The regression analysis demonstrated a high accuracy with a mean absolute error (MAE) of 2.62 kg, an error rate of 8.91%, and a coefficient of determination of 0.802.
Conclusions:
The proposed method demonstrates that smartphones can serve as a viable, pervasive tool for daily grip strength monitoring, offering a convenient alternative to traditional dynamometers.
Citation
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Copyright
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