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A Metamaterial Computational Multi-Sensor of Grip-Strength Properties with Point-of-Care Human-Computer Interaction.

Yinghua Chen1, Tianrun Li1, Zhemin Wang1

  • 1State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|October 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel metamaterial sensor for comprehensive hand grip assessment, measuring force, speed, and endurance. This intelligent system enhances point-of-care diagnostics and interactive healthcare for improved global health outcomes.

Keywords:
grip strengthhuman-computer interactionmechanical metamaterialspiezoelectricityself-powered sensing

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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Computational Health

Background:

  • Grip strength is a key indicator for frailty, brain health, cardiovascular, and psychological well-being.
  • Developing reliable, interactive, point-of-care devices for multi-sensing hand grip status remains a significant challenge.

Purpose of the Study:

  • To uncover the relationship between soft buckling metamaterial deformations and piezoelectric voltage signals for multi-dimensional grip sensing.
  • To establish an intelligent metamaterial computational sensor system for comprehensive hand grip assessment.
  • To integrate human-computer interactive games for enhanced user compliance in grip measurements.

Main Methods:

  • Utilized a hyperelastic model for mechanical characterization of the metamaterial.
  • Employed machine learning models for computational sensing capabilities.
  • Developed a graphical user interface for visual feedback and interactive gaming.

Main Results:

  • Demonstrated a novel method for sensing maximal grip force, grip speed, grip impulse, and endurance indicators.
  • Successfully conducted grip measurements on elderly individuals, incorporating interactive games.
  • Showcased real-time, point-of-care grip assessment and training for potential sarcopenia screening in patients.

Conclusions:

  • The developed intelligent metamaterial sensor offers a reliable and interactive solution for comprehensive hand grip assessment.
  • This system advances human-computer interactive healthcare and establishes a point-of-care biomechanical platform.
  • The technology holds potential for widespread application in health monitoring and early disease detection, contributing to global health.