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A Novel Application of Musculoskeletal Ultrasound Imaging
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Simultaneous Estimation of Hand Configurations and Finger Joint Angles Using Forearm Ultrasound.

Keshav Bimbraw1, Christopher J Nycz2, Matthew Schueler1

  • 1Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 USA.

IEEE Transactions on Medical Robotics and Bionics
|June 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning pipeline using forearm ultrasound images to predict metacarpophalangeal (MCP) joint angles and classify hand configurations for intuitive human-machine interaction.

Keywords:
AI-enabled roboticsand facial expressionsdesign and development of robots for human-robot interactiongesturehuman-machine interfaces and robotics applicationsnew technologies and methodologies in medical roboticspostureuser-centered design and applicationswearable roboticswearable sensor systems

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Biomedical Engineering
  • Machine Learning and Deep Learning

Background:

  • Advancements in computing and robotics necessitate intuitive human-machine interfaces.
  • Hand motion recognition, including hand configuration classification and metacarpophalangeal (MCP) joint angle detection, is crucial for seamless interaction with digital and physical systems.
  • While surface electromyography (sEMG) and other methods exist, forearm ultrasound offers a non-invasive musculoskeletal visualization for hand motion analysis.

Purpose of the Study:

  • To develop and evaluate a deep learning pipeline for estimating MCP joint angles from forearm ultrasound images.
  • To classify hand configurations using machine learning algorithms applied to ultrasound data.
  • To address the gap in literature regarding the simultaneous estimation of hand configuration and MCP joint angles from forearm ultrasound.

Main Methods:

  • A convolutional neural network (CNN) based deep learning pipeline was proposed for predicting MCP joint angles.
  • Ultrasound images were acquired from subjects performing 11 distinct hand configurations.
  • Hand configuration classification was compared using Support Vector Classifier (SVC), multi-layer perceptron, and the proposed CNN. Motion capture data provided ground truth for joint angles.

Main Results:

  • The proposed CNN achieved an average classification accuracy of 82.7 ± 9.7% for hand configurations.
  • Support Vector Classifier (SVC) with different kernels also demonstrated over 80% classification accuracy.
  • An average Root Mean Square Error (RMSE) of 7.35°±1.3° was achieved for predicted MCP joint angles.

Conclusions:

  • The developed CNN-based pipeline effectively estimates MCP joint angles and classifies hand configurations from forearm ultrasound images.
  • The system achieves low latency (6.25 - 9.1 Hz), making it suitable for real-time control applications.
  • This approach offers a promising method for enhancing intuitive control in human-machine interfaces and robotic systems.