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Real-time signal processing in MEMS sensor-based motion analysis systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. -. (-)ORCID iD: 0000-0002-4947-5037
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: urn:nbn:se:mdh:diva-42619ISBN: 978-91-7485-421-3 (print)OAI: oai:DiVA.org:mdh-42619DiVA, id: diva2:1286907
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: 2019-02-19Bibliographically approved
List of papers
1. Signal processing algorithms for temperauture drift in a MEMS-gyro-based head mouse
Open this publication in new window or tab >>Signal processing algorithms for temperauture drift in a MEMS-gyro-based head mouse
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.

Series
International Conference on Systems, Signals, and Image Processing, ISSN 2157-8702
Keywords
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:nbn:se:mdh:diva-25745 (URN)000397392000010 ()2-s2.0-84904006787 (Scopus ID)9789531841917 (ISBN)
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: 2019-02-08Bibliographically approved
2. Signal processing algorithms for position measurement with MEMS-based accelerometer
Open this publication in new window or tab >>Signal processing algorithms for position measurement with MEMS-based accelerometer
2015 (English)In: IFMBE Proceedings, vol. 48, 2015, p. 36-39Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents signal processing algorithms for position measurements with MEMS-accelerometers in a motion analysis system. The motion analysis system is intended to analyze the human motion with MEMS-based-sensors which is a part of embedded sensor systems for health. MEMS-accelerometers can be used to measure acceleration and theoretically the velocity and position can be derived from the integration of acceleration. However, there normally is drift in the measured acceleration, which is enlarged under integration. In this paper, the signal processing algorithms are used to minimize the drift during integration by MEMS-based accel-erometer. The simulation results show that the proposed algorithms improved the results a lot. The algorithm reduced the drift in one minute by about 20 meters in the simulation. It can be seen as a reference of signal processing for the motion analysis system with MEMS-based accelerometer in the future work.

Keywords
Accelerometers; Algorithms; Biomedical engineering; Embedded systems; Integration; MEMS; Microelectromechanical devices; Position measurement
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-26833 (URN)10.1007/978-3-319-12967-9_10 (DOI)000347893000010 ()2-s2.0-84910660675 (Scopus ID)9783319129662 (ISBN)
Conference
16th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics and Medicinteknikdagarna Joint Conferences, NBC 2014 and MTD 2014; Gothenburg; Sweden; 14 October 2014 through 16 October 2014
Available from: 2014-12-05 Created: 2014-12-05 Last updated: 2019-02-08Bibliographically approved
3. Noise reduction for a MEMS-­gyroscope-­based head mouse
Open this publication in new window or tab >>Noise reduction for a MEMS-­gyroscope-­based head mouse
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, p. 98-104Conference 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
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 211
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-28166 (URN)10.3233/978-1-61499-516-6-98 (DOI)2-s2.0-84939224838 (Scopus ID)978-1-61499-515-9 (ISBN)
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: 2019-02-08Bibliographically approved
4. The effects of perceived USB-delay for sensor and embedded system development
Open this publication in new window or tab >>The effects of perceived USB-delay for sensor and embedded system development
Show others...
2016 (English)In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSVolume 2016, 2016, p. 2492-2495, article id 7591236Conference paper, Published paper (Refereed)
Abstract [en]

Perceiving delay in computer input devices is a problem which gets even more eminent when being used in healthcare applications and/or in small, embedded systems. Therefore, the amount of delay found as acceptable when using computer input devices was investigated in this paper. A device was developed to perform a benchmark test for the perception of delay. The delay can be set from 0 to 999 milliseconds (ms) between a receiving computer and an available USB-device. The USB-device can be a mouse, a keyboard or some other type of USB-connected input device. Feedback from performed user tests with 36 people form the basis for the determination of time limitations for the USB data processing in microprocessors and embedded systems without users' noticing the delay. For this paper, tests were performed with a personal computer and a common computer mouse, testing the perception of delays between 0 and 500 ms. The results of our user tests show that perceived delays up to 150 ms were acceptable and delays larger than 300 ms were not acceptable at all.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-32878 (URN)10.1109/EMBC.2016.7591236 (DOI)000399823502209 ()2-s2.0-85009097778 (Scopus ID)9781457702204 (ISBN)
Conference
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC'16, 15 Aug 2016, Orlando, United States
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2016-08-30 Created: 2016-08-24 Last updated: 2019-02-08Bibliographically approved
5. Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems
Open this publication in new window or tab >>Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems
Show others...
2016 (English)In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, Västeraås, Sweden, 2016, Vol. 187, p. 107-112Conference paper, Published paper (Refereed)
Abstract [en]

New computer input devices in healthcare applications using small embedded sensors need firmware filters to run smoothly and to provide a better user experience. Therefore, it has to be investigated how much delay can be tolerated for signal processing before the users perceive a delay when using a computer input device. This paper is aimed to find out a threshold of unperceived delay by performing user tests with 25 participants. A communication retarder was used to create delays from 0 to 100 ms between a receiving computer and three different USB-connected computer input devices. A wired mouse, a wifi mouse and a head-mounted mouse were used as input devices. The results of the user tests show that delays up to 50ms could be tolerated and are not perceived as delay, or depending on the used device still perceived as acceptable.

Place, publisher, year, edition, pages
Västeraås, Sweden: , 2016
Series
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Keywords
Author keywords Computer mouse; Delay; Embedded systems; Healthcare; Perception; USB Indexed keywords Engineering controlled terms: Embedded systems; Firmware; Health care; Internet of things; Knobs; Mammals; Medical computing; Sensory perception Computer input devices; Computer mouse; Delay; Embedded sensors; Health care application; Input devices; Motion sensors; User experience Engineering main heading: Signal processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-33810 (URN)10.1007/978-3-319-51234-1_17 (DOI)000428954100017 ()2-s2.0-85011301202 (Scopus ID)
Conference
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västeraås, Sweden
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2016-11-21 Created: 2016-11-21 Last updated: 2019-02-08Bibliographically approved
6. A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse
Open this publication in new window or tab >>A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse
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. 

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-35032 (URN)10.1016/j.bspc.2017.02.013 (DOI)000401209300004 ()2-s2.0-85014528467 (Scopus ID)
Available from: 2017-03-16 Created: 2017-03-16 Last updated: 2019-02-08Bibliographically approved
7. Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review
Open this publication in new window or tab >>Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review
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
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-39031 (URN)10.3390/s18041123 (DOI)000435574800195 ()29739337 (PubMedID)2-s2.0-85045087893 (Scopus ID)
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2019-02-08Bibliographically approved

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