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A Review of Force Myography Research and Development
1Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, Canada. zgx@sfu.ca.
Sensors (Basel, Switzerland)
|October 23, 2019
Summary
Force Myography (FMG) uses muscle stiffness patterns to predict limb movements for activity monitoring and human-machine interfaces. This review covers 20 years of FMG research, highlighting hardware, signal processing, and future challenges.
Area of Science:
- Biomedical Engineering
- Rehabilitation Engineering
- Human-Computer Interaction
Background:
- Force Myography (FMG) is an emerging technique for capturing limb movement information.
- FMG utilizes force sensors to detect variations in muscle stiffness during movement.
- This method shows potential for physical activity monitoring and human-machine interface (HMI) applications.
Purpose of the Study:
- To provide a comprehensive review of Force Myography (FMG) technology over the past two decades.
- To summarize advancements in FMG hardware design and signal processing techniques.
- To identify current challenges and future directions for FMG implementation in real-world scenarios.
Main Methods:
- Systematic literature review of research on Force Myography (FMG) published in the last 20 years.
- Analysis of studies focusing on FMG sensor technology, including hardware configurations and materials.
- Examination of signal processing algorithms and machine learning approaches applied to FMG data for activity recognition.
Main Results:
- Significant progress has been made in FMG hardware miniaturization and sensor sensitivity.
- Advanced signal processing techniques, including deep learning, have improved the accuracy of limb activity prediction.
- FMG has demonstrated potential in diverse applications ranging from prosthetic control to physical rehabilitation.
Conclusions:
- Force Myography (FMG) technology has matured significantly over the last 20 years.
- Further research is needed to address challenges related to robustness, user adaptability, and widespread adoption.
- FMG holds considerable promise for future applications in healthcare and human-computer interaction.
