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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Frequency domain surface EMG sensor fusion for estimating finger forces.

Chandrasekhar Potluri1, Parmod Kumar, Madhavi Anugolu

  • 1Measurement and Control Engineering Research Center (MCERC), Idaho State University, Pocatello, Idaho 83201, USA. potlchan@isu.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
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This study improves hand and finger force estimation using surface electromyography (sEMG) array sensors and a novel sensor fusion technique. The new method enhances accuracy by addressing signal challenges like cross-talk and noise.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Signal Processing

Background:

  • Estimating skeletal hand/finger forces from surface electromyography (sEMG) signals is challenging due to signal noise, cross-talk, and complex modulation.
  • Traditional sEMG analysis relies on single sensor data, limiting accuracy in complex movements.

Purpose of the Study:

  • To develop and validate a sensor fusion scheme using sEMG array sensors for improved skeletal hand/finger force estimation.
  • To address the limitations of single-sensor sEMG measurements in force prediction.

Main Methods:

  • Utilized sEMG array sensors instead of single sensors.
  • Developed a sensor fusion scheme to create a Multi-Input-Single-Output (MISO) transfer function.
  • Employed system identification and a Genetic Algorithm (GA) to optimize the MISO system parameters.

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Last Updated: Jun 6, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Force and Position Control in Humans - The Role of Augmented Feedback
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Published on: June 19, 2016

Main Results:

  • The proposed sensor fusion approach, utilizing MISO transfer functions, was experimentally validated.
  • Demonstrated improved accuracy in estimating finger and hand forces compared to conventional methods.
  • The Genetic Algorithm effectively optimized the MISO system for enhanced performance.

Conclusions:

  • Sensor fusion with sEMG array sensors offers a significant improvement for skeletal hand/finger force estimation.
  • The developed MISO system, optimized via GA, provides a robust framework for accurate force prediction.
  • This approach holds promise for advanced prosthetic control and human-computer interfaces.