Abstract
Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.
Highlights
In contemporary/modern society, demographic changes of population and multiple diseases lead to increasing demands on and costs for the healthcare systems [1]
The aim of this paper is to present a systematic review of the signal error reduction algorithms/methods that are used for Microelectromechanical system (MEMS) gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area
All algorithms/methods were aimed at reducing different signal errors for the MEMS gyroscope-based human motion analysis system
Summary
In contemporary/modern society, demographic changes of population and multiple diseases lead to increasing demands on and costs for the healthcare systems [1]. MEMS technology has enabled the development of miniaturized inertial sensors, which have been used in motor activity and other health status monitoring systems [4]. They have already been widely applied in motion analysis systems in the medical field for knee/ankle joint measurement [5,6,7,8], gait analysis [9,10], ambulatory measurement and analysis of the lower limbs [11,12], the collection of anatomical joint angles during stair ascent [13], hand gesture recognition [14,15,16], head-motion-controlled
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
