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Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB, Västerås, Sweden.ORCID iD: 0000-0002-4947-5037
Motion Control i Västerås AB, Västerås, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 4, article id 1123Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 18, no 4, article id 1123
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-39031DOI: 10.3390/s18041123ISI: 000435574800195PubMedID: 29739337Scopus ID: 2-s2.0-85045087893OAI: oai:DiVA.org:mdh-39031DiVA, id: diva2:1198704
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research ProfileAvailable from: 2018-04-18 Created: 2018-04-18 Last updated: 2018-10-16Bibliographically approved

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Publisher's full textPubMedScopushttp://www.mdpi.com/1424-8220/18/4/1123/htm

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Du, JiayingLindén, Maria

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