Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review

Area of Science:

• Engineering
• Signal Processing
• Biomedical Engineering

Background:

• Micro-Electro-Mechanical Systems (MEMS) gyroscopes offer small size, high sensitivity, and low cost for motion analysis.
• Environmental factors like temperature, shock, and vibration introduce errors in MEMS gyroscope data.
• Effective signal processing is crucial for accurate human motion analysis using MEMS gyroscopes.

Purpose of the Study:

• To systematically review signal error reduction algorithms for MEMS gyroscope-based human motion analysis.
• To identify and categorize algorithms suitable for improving signal quality and system stability.
• To provide a guide for selecting appropriate signal processing techniques.

Main Methods:

• Systematic literature search across ACM Digital Library, IEEE Xplore, PubMed, and Scopus.
• Review of 16 relevant papers published in the last 10 years.
• Categorization of 17 identified algorithms into four groups: Kalman-filter-based, adaptive-based, simple filter, and compensation-based.

Main Results:

• Seventeen signal error reduction algorithms were analyzed.
• Algorithms were grouped based on their processing approach.
• Characteristics, advantages, disadvantages, and time limitations of each algorithm were presented.

Conclusions:

• A comprehensive overview of signal processing algorithms for MEMS gyroscope motion analysis is provided.
• The review facilitates the selection of optimal algorithms for human motion analysis applications.
• This work aids in enhancing the accuracy and reliability of MEMS gyroscope-based systems.