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Noise reduction for a MEMS-­gyroscope-­based head mouse
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4947-5037
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB.ORCID iD: 0000-0003-1940-1747
2015 (English)In: Studies in Health Technology and Informatics, Volume 211: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, Västerås, Sweden: IOS Press , 2015, 98-104 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, four different signal processing algorithms which can be applied to reduce the noise from a MEMS-gyroscope-based computer head mouse are presented. MEMS-gyroscopes are small, light, cheap and widely used in many electrical products. MultiPos, a MEMS-gyroscope-based computer head mouse system was designed for persons with movement disorders. Noise such as physiological tremor and electrical noise is a common problem for the MultiPos system. In this study four different signal processing algorithms were applied and evaluated by simulation in MATLAB and implementation in a dsPIC, with aim to minimize the noise in MultiPos. The algorithms were low-pass filter, Least Mean Square (LMS) algorithm, Kalman filter and Weighted Fourier Linear Combiner (WFLC) algorithm. Comparisons and system tests show that these signal processing algorithms can be used to improve the MultiPos system. The WFLC algorithm was found the best method for noise reduction in the application of a MEMS-gyroscope-based head mouse.

Place, publisher, year, edition, pages
Västerås, Sweden: IOS Press , 2015. 98-104 p.
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 211
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-28166DOI: 10.3233/978-1-61499-516-6-98Scopus ID: 2-s2.0-84939224838ISBN: 978-1-61499-515-9 (print)OAI: oai:DiVA.org:mdh-28166DiVA: diva2:818133
Conference
2th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2015-06-08 Created: 2015-06-08 Last updated: 2016-03-21Bibliographically approved
In thesis
1. Signal processing for MEMS sensor based motion analysis system
Open this publication in new window or tab >>Signal processing for MEMS sensor based motion analysis system
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Sensor systems for motion analysis represent an important class of embeddedsensor systems for health, and are usually based on MEMS technology(Micro-electro-mechanical systems). Gyroscopes and accelerometers are two examples of MEMS motion sensors that are characterized by their small size,low weight, low power consumption, and low cost. This makes them suitableto be used in wearable systems, intended to measure body movements and posture,and to provide the input for advanced human motion analyzes. However,MEMS-sensors usually are sensitive to environmental disturbances, such as shock, vibration and temperature changes. A large portion of the measured MEMS-sensor signal actually origins from error sources such as noise, offset, and drift. Especially, temperature drift is a well-known error source. Accumulation errors increase the effect of the error during integration of acceleration orangular rate to determine the position or angle. Thus, methods to limit or eliminate the influence of the sources of errors are urgent. Due to MEMS-sensor characteristics and the measurement environment in human motion analysis,signal processing is regarded as an important and necessary part of a MEMS-sensor based human motion analysis system.

This licentiate thesis focuses on signal processing for MEMS-sensor based human motion analysis systems. Different signal processing algorithms were developed, comprising noise reduction, offset/drift estimation and reduction,position accuracy and system stability. Further, real time performance was achieved, also fulfilling the hardware requirement of limited calculation capacity.High-pass filter, LMS algorithm and Kalman filter were used to reduce offset, drift and especially temperature drift in a MEMS-gyroscope based system,while low-pass filter, LMS algorithm, Kalman filter and WFLC algorithms were used for noise reduction. Simple methods such as thresholding with delay and velocity estimation were developed to improve the signal during the position measurements. A combination method of Kalman filter, WFLC algorithm and thresholding with delay was developed with focus on the static stability and position accuracy of the MEMS-gyroscope based system. These algorithms have been implemented into a previously developed MEMS-sensorbased motion analysis system. The computational times of the algorithms were all acceptable. Kalman filtering was found efficient to reduce the problem of temperature drift and the WFLC algorithm was found the most suitable method to reduce human physiological tremor and electrical noise. With the Trapezoidal method and low-pass filter, threshold with delay method and velocity estimation method reduced integrated drift in one minute by about 20 meters for the position measurements with MEMS-accelerometer. The threshold with delay method made the signal around zero level to zero without interrupting the continuous movement signal. The combination method of Kalman filter,WFLC algorithm and threshold with delay method showed its superiority on improving the static stability and position accuracy by reducing noise, offset and drift simultaneously, 100% error reduction during the static state, 98.2%position error correction in the case of movements without drift, and 99% with drift.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2016
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 228
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-31298 (URN)978-91-7485-256-1 (ISBN)
Presentation
2016-05-02, Gamma, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2016-03-21 Created: 2016-03-17 Last updated: 2016-04-06Bibliographically approved

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Du, JiayingLindén, Maria

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