mdh.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 10) Show all publications
Du, J. (2019). Real-time signal processing in MEMS sensor-based motion analysis systems. (Doctoral dissertation). Västerås: Mälardalen University
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
Du, J., Gerdtman, C. & Lindén, M. (2018). Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review. Sensors, 18(4), Article ID 1123.
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
Du, J., Gerdtman, C., Gharehbaghi, A. & Lindén, M. (2017). A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse. Biomedical Signal Processing and Control, 35, 30-37
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
Du, J., Gerdtman, C. & Lindén, M. (2017). Development of a MEMS-sensor based motion analysis system for human movement rehabilitation. In: International conference on movement: brain, body, cognition Movement2017: . Paper presented at International conference on movement: brain, body, cognition Movement2017, 09 Jul 2017, Oxford, United Kingdom.
Open this publication in new window or tab >>Development of a MEMS-sensor based motion analysis system for human movement rehabilitation
2017 (English)In: International conference on movement: brain, body, cognition Movement2017, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37080 (URN)
Conference
International conference on movement: brain, body, cognition Movement2017, 09 Jul 2017, Oxford, United Kingdom
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-10-31Bibliographically approved
Du, J., Gerdtman, C. & Lindén, M. (2017). Signal processing to improve the MEMS sensor signal in a small embedded sensor system for health. In: Medicinteknikdagarna 2017 MTD 2017: . Paper presented at Medicinteknikdagarna 2017 MTD 2017, 09 Oct 2017, Västerås, Sweden.
Open this publication in new window or tab >>Signal processing to improve the MEMS sensor signal in a small embedded sensor system for health
2017 (English)In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper, Published paper (Refereed)
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37081 (URN)
Conference
Medicinteknikdagarna 2017 MTD 2017, 09 Oct 2017, Västerås, Sweden
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-10-31Bibliographically approved
Du, J. (2016). Signal processing for MEMS sensor based motion analysis system. (Licentiate dissertation). Västerås: Mälardalen University
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
Du, J., Kade, D., Gerdtman, C., Özcan, O. & Lindén, M. (2016). The effects of perceived USB-delay for sensor and embedded system development. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSVolume 2016: . Paper presented at 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC'16, 15 Aug 2016, Orlando, United States (pp. 2492-2495). , Article ID 7591236.
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
Du, J., Gerdtman, C. & Lindén, M. (2015). Noise reduction for a MEMS-­gyroscope-­based head mouse. 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. Paper presented at 2th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden (pp. 98-104). Västerås, Sweden: IOS Press
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)000455821300007 ()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: 2019-06-18Bibliographically approved
Du, J., Gerdtman, C. & Lindén, M. (2015). Signal processing algorithms for position measurement with MEMS-based accelerometer. In: IFMBE Proceedings, vol. 48: . Paper presented at 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 (pp. 36-39).
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
Du, J., Gerdtman, C. & Lindén, M. (2014). Signal processing algorithms for temperauture drift in a MEMS-gyro-based head mouse. In: Int. Conf. Syst. Signals Image Process.: . Paper presented at 21st International Conference on Systems, Signals and Image Processing, IWSSIP 2014, 12 May 2014 through 15 May 2014, Dubrovnik (pp. 123-126).
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4947-5037

Search in DiVA

Show all publications