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Related Experiment Video

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A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb

Paolo Bonifati1, Marco Baracca1, Mariangela Menolotto1

  • 1Research Center "E. Piaggio", Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56126 Pisa, Italy.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study presents an under-sensorized wearable system for reconstructing upper limb musculoskeletal state. Using only two inertial measurement units (IMUs) and eight surface electromyography (sEMG) sensors, it accurately captures joint and muscle dynamics.

Keywords:
IMUsSensor Fusionhuman multimodal motion trackingoptimal designsEMG sensorsupper limbwearable sensing

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Human Motion Analysis

Background:

  • Wearable sensing offers unobtrusive human musculoskeletal monitoring.
  • Reducing sensor count is crucial for deployability, cost, and wearability.
  • Previous theoretical work addressed reconstructing upper limb state with limited optimal sensor data.

Purpose of the Study:

  • To bridge the gap between theoretical solutions and practical implementation of under-sensorized wearable systems.
  • To develop and validate an under-sensorized system for reconstructing the upper limb musculoskeletal state.
  • To minimize the number of sensors required for comprehensive state reconstruction.

Main Methods:

  • Development of an under-sensorized wearable system using inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes.
  • Focus on minimizing the number of IMUs and sEMG sensors for upper limb state reconstruction.
  • Jointly reconstructing 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state.

Main Results:

  • Successful reconstruction of the entire upper limb musculoskeletal state using only two IMUs and eight sEMG sensors.
  • Achieved a median normalized RMS error of 8.5% for non-measured joints.
  • Achieved a median normalized RMS error of 2.5% for non-measured muscles.

Conclusions:

  • An under-sensorized wearable system can effectively reconstruct the upper limb musculoskeletal state with minimal sensors.
  • The proposed system demonstrates high accuracy in capturing joint and muscle dynamics.
  • This approach enhances the deployability and practicality of wearable musculoskeletal monitoring.