School of Innovation, Design and Engineering, Mälardalen University, 721 23 Västerås, Sweden.
Motion Control i Västerås AB, 721 30 Västerås, Sweden.
Sensors (Basel). 2018 Apr 6;18(4):1123. doi: 10.3390/s18041123.
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.
运动传感器，如微机电系统（MEMS）陀螺仪和加速度计，具有体积小、重量轻、灵敏度高和成本低的特点。它们在越来越多的应用中得到了应用。然而，它们很容易受到环境影响，如温度变化、冲击和振动。因此，信号处理对于最小化误差、提高信号质量和系统稳定性至关重要。本工作旨在研究和介绍用于基于 MEMS 陀螺仪的运动分析系统的不同信号误差减少算法，这些系统用于人体运动分析或有可能用于该领域。通过 ACM 数字图书馆、IEEE Xplore、PubMed 和 Scopus 的搜索引擎/数据库进行了系统搜索。发现并全面审查了过去 10 年在期刊或会议论文集中发表的 16 篇专注于 MEMS 陀螺仪相关信号处理的论文。将 17 种算法分为四大类：基于卡尔曼滤波的算法、基于自适应的算法、简单滤波器算法和基于补偿的算法。对算法进行了分析，并介绍了其特点，如优点、缺点和时间限制。提出了在该领域内最适合的信号处理算法的用户指南。
