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Related Experiment Video

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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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Simplifying Prediction of Intended Grasp Type: Accelerometry Performs Comparably to Combined EMG-Accelerometry in

Samira Afshari1, Rachel V Vitali1, Deema Totah1

  • 1Department of Mechanical Engineering, University of Iowa, Iowa City, IA 52242, USA.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

Accelerometry (ACC) sensors are sufficient for accurate prosthetic hand grasp prediction in amputees, outperforming electromyography (EMG) sensors. This finding supports simpler, user-friendly prosthetic designs.

Keywords:
accelerometryelectromyographyhand gesture recognitionprostheticswearable sensing

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Active upper-limb prostheses face adoption challenges due to bulky designs and complex operation.
  • Accurate gesture prediction using minimal sensors is crucial for developing user-friendly prosthetic devices.
  • Wearable sensors like electromyography (EMG) and accelerometry (ACC) offer valuable data for prosthetic control.

Purpose of the Study:

  • To determine whether EMG or ACC sensors provide more valuable information for predicting hand grasps.
  • To identify key signal features that contribute most to grasp prediction accuracy.
  • To compare sensor performance between individuals with and without amputation.

Main Methods:

  • Utilized an open-source dataset containing EMG and ACC signals.
  • Trained subject-specific Linear Discriminant Analysis (LDA) and K-Nearest Neighbors (KNN) classifiers.
  • Predicted 10 distinct grasp types using 4-fold cross-validation across 13 individuals with amputation and 28 able-bodied individuals.

Main Results:

  • LDA classifiers achieved 84.7% accuracy using ACC features alone, comparable to combined ACC and EMG (88.3%), but significantly higher than EMG alone (58.1%).
  • Individuals with amputation achieved >80% accuracy with only three features (two ACC-derived).
  • Able-bodied participants required nine features, with a greater reliance on EMG signals.

Conclusions:

  • Accelerometry (ACC) sensors provide sufficient data for robust grasp classification in individuals with amputation.
  • ACC-based grasp prediction can facilitate the development of simpler and more accessible prosthetic designs.
  • Future research should integrate object recognition and grip force control for advanced prosthetic functionality.