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Grip Strength Estimation Using Input Data From a Commodity Smartphone: Model Development and Validation Study.

Komei Tajima1, Kaori Ikematsu2, Toshiya Isomoto2

  • 1Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Yokohama, Japan.

JMIR Human Factors
|April 30, 2026
PubMed
Summary
This summary is machine-generated.

This study shows smartphones can estimate grip strength using touch and sensor data, offering a convenient alternative to dynamometers for monitoring muscle health.

Keywords:
grip strengthhuman computer interactionmachine learningmobile healthsmartphone

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Health Monitoring

Background:

  • Grip strength is a key indicator for muscle deterioration, sarcopenia, and neurological conditions.
  • Traditional grip strength measurement requires specialized, inaccessible dynamometers.

Purpose of the Study:

  • To develop and validate a smartphone-based method for estimating grip strength.
  • To eliminate the need for dedicated grip strength measurement devices.

Main Methods:

  • Collected grip strength and smartphone interaction data (tapping, flicking, dragging) from 21 adults.
  • Developed a predictive regression model using touch and inertial sensor data.
  • Evaluated model accuracy via random split, leave-one-user-out, and few-day calibration validation.

Main Results:

  • Random split evaluation showed high accuracy (MAE 2.62 kg, MAPE 8.91%).
  • Leave-one-user-out validation yielded a MAPE of 15.08%.
  • Personalized calibration improved accuracy, reducing MAPE to 11.64% after 4 days; subjective workload was low.

Conclusions:

  • Smartphones offer a viable and accessible tool for daily grip strength monitoring.
  • The proposed method provides a convenient alternative to traditional dynamometers.
  • This technology supports pervasive health monitoring without user burden.