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Signal processing algorithms for temperauture drift in a MEMS-gyro-based head mouse
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4947-5037
Motion Control i Vasteras AB, Vasteras, Sweden .
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
2014 (English)In: Int. Conf. Syst. Signals Image Process., 2014, p. 123-126Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a comparison between different signal processing algorithms applied to a gyro-based computer head mouse for persons with movement disorders. MEMS-gyros can be used to sense the head movement and rotation. However, the measured gyro signals are influenced by noise, offset, drift and especially temperature drift. Thus, there is a need to improve the signal by signal processing algorithms. Different gyros have different characteristics and the algorithms should be useful for any selected MEMS-gyro. In this paper, three different signal processing algorithms were designed and evaluated by simulation in MATLAB and implementation in a dsPIC, with the aim to compensate for the temperature drift problem. The algorithms are high-pass filtering, Kalman algorithm and Least Mean Square (LMS) algorithm. Comparisons and system test show that these filters can be used for temperature drift compensation and the Kalman filter showed the best in the application of a MEMS-gyro-based computer head mouse.

Place, publisher, year, edition, pages
2014. p. 123-126
Series
International Conference on Systems, Signals, and Image Processing, ISSN 2157-8702
Keyword [en]
High-pass, Kalman, LMS, MEMS-gyros, signal processing, Gyroscopes, Image processing, Mammals, MATLAB, Least mean square algorithms, Signal processing algorithms, Simulation in matlabs, Temperature drift compensation, Algorithms
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-25745Scopus ID: 2-s2.0-84904006787ISBN: 9789531841917 (print)OAI: oai:DiVA.org:mdh-25745DiVA, id: diva2:736781
Conference
21st International Conference on Systems, Signals and Image Processing, IWSSIP 2014, 12 May 2014 through 15 May 2014, Dubrovnik
Available from: 2014-08-08 Created: 2014-08-04 Last updated: 2018-02-23Bibliographically 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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Citation style
  • apa
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