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A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB, Västerås.ORCID iD: 0000-0002-4947-5037
Motion Control i Västerås AB, Västerås.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
2017 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, p. 30-37Article in journal (Refereed) Published
Abstract [en]

This paper presents a signal processing algorithm to remove different types of noise from a gyroscopic head-borne computer mouse. The proposed algorithm is a combination of a Kalman filter (KF), a Weighted-frequency Fourier Linear Combiner (WFLC) and a threshold with delay method (TWD). The gyroscopic head-borne mouse was developed to assist persons with movement disorders. However, since MEMS-gyroscopes are usually sensitive to environmental disturbances such as shock, vibration and temperature change, a large portion of noise is added at the same time as the head movement is sensed by the MEMS-gyroscope. The combined method is applied to the specially adapted mouse, to filter out different types of noise together with the offset and drift, with marginal need of the calculation capacity. The method is examined with both static state tests and movement operation tests. Angular position is used to evaluate the errors. The results demonstrate that the combined method improved the head motion signal substantially, with 100.0% error reduction during the static state, 98.2% position error correction in the case of movements without drift and 99.9% with drift. The proposed combination in this paper improved the static stability and position accuracy of the gyroscopic head-borne mouse system by reducing noise, offset and drift, and also has the potential to be used in other gyroscopic sensor systems to improve the accuracy of signals. 

Place, publisher, year, edition, pages
2017. Vol. 35, p. 30-37
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-35032DOI: 10.1016/j.bspc.2017.02.013ISI: 000401209300004Scopus ID: 2-s2.0-85014528467OAI: oai:DiVA.org:mdh-35032DiVA, id: diva2:1082403
Available from: 2017-03-16 Created: 2017-03-16 Last updated: 2019-02-08Bibliographically approved
In thesis
1. Real-time signal processing in MEMS sensor-based motion analysis systems
Open this publication in new window or tab >>Real-time signal processing in MEMS sensor-based motion analysis systems
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This PhD thesis focuses on real-time signal processing for hardware-limited micro-electro-mechanical system (MEMS) sensor-based human motion analysis systems. The aim of the thesis is to improve the signal quality of MEMS gyroscopes and accelerometers by minimizing the effects of signal errors, considering the hardware limitations and the users' perception.

MEMS sensors such as MEMS gyroscopes and MEMS accelerometers are important components in motion analysis systems. They are known for their small size, light weight, low power consumption, low cost, and high sensitivity. This makes them suitable for wearable systems for measuring body movements. The data can further be used as input for advanced human motion analyses. However, MEMS sensors are usually sensitive to environmental disturbances such as shock, vibration, and temperature change. A large portion of the MEMS sensor signals actually originate from error sources such as noise, offset, null drift and temperature drift, as well as integration drift. Signal processing is regarded as the major key solution to reduce these errors. For real-time signal processing, the algorithms need to be executed within a certain specified time limit. Two crucial factors have to be considered when designing real-time signal processing algorithms for wearable embedded sensor systems. One is the hardware limitations leading to a limited calculation capacity, and the other is the user perception of the delay caused by the signal processing.

Within this thesis, a systematic review of different signal error reduction algorithms for MEMS gyroscope-based motion analysis systems for human motion analysis is presented. The users’ perceptions of the delay when using different computer input devices were investigated. 50 ms was found as an acceptable delay for the signal processing execution in a real-time motion analysis system. Real-time algorithms for noise reduction, offset/drift estimation and reduction, improvement of position accuracy and system stability considering the above mentioned requirements, are presented in this thesis. The algorithms include a simplified high-pass filter and low-pass filter, a LMS algorithm, a Kalman filter, a WFLC algorithm, two simple novel algorithms (a TWD method and a velocity drift estimation method), and a novel combination method KWT.  Kalman filtering was found to be 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. The TWD method resulted in a signal level around zero without interrupting the continuous movement signal. The combination method improved the static stability and the position accuracy considerably.  The computational time for the execution of the algorithms were all perceived as acceptable by users and kept within the specified time limit for real-time performance.  Implementations and experiments showed that these algorithms are feasible for establishing high signal quality and good system performance in previously developed systems, and also have the potential to be used in similar systems.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 285
National Category
Signal Processing
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-42619 (URN)978-91-7485-421-3 (ISBN)
Public defence
2019-03-19, Gamma, Mälardalens högskola, Västerås, 09:30 (English)
Opponent
Supervisors
Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-09-20Bibliographically approved

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Du, JiayingLindén, Maria

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