mdh.sePublications
Change search
Refine search result
1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Du, Jiaying
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Real-time signal processing in MEMS sensor-based motion analysis systems2019Doctoral 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.

  • 2.
    Du, Jiaying
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Signal processing for MEMS sensor based motion analysis system2016Licentiate 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.

  • 3.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB, Västerås.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås.
    Gharehbaghi, Arash
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse2017In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, p. 30-37Article in journal (Refereed)
    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. 

  • 4.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås, Sweden .
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Signal processing algorithms for position measurement with MEMS-based accelerometer2015In: IFMBE Proceedings, vol. 48, 2015, p. 36-39Conference 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.

  • 5.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, C.
    Motion Control i Vasteras AB, Vasteras, Sweden .
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Signal processing algorithms for temperauture drift in a MEMS-gyro-based head mouse2014In: Int. Conf. Syst. Signals Image Process., 2014, p. 123-126Conference 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.

  • 6.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB, Västerås, Sweden.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås, Sweden.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 4, article id 1123Article in journal (Refereed)
    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.

  • 7.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, Christer
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Development of a MEMS-sensor based motion analysis system for human movement rehabilitation2017In: International conference on movement: brain, body, cognition Movement2017, 2017Conference paper (Refereed)
  • 8.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, Christer
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB.
    Noise reduction for a MEMS-­gyroscope-­based head mouse2015In: 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 (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.

  • 9.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, Christer
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Signal processing to improve the MEMS sensor signal in a small embedded sensor system for health2017In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper (Refereed)
  • 10.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kade, Daniel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB, Västerås, Sweden.
    Gerdtman, Christer
    Motion Control i Västerås AB, Västerås, Sweden.
    Lindell, Rikard
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ozcan, Oguzhan
    Arçelik Research Center for Creative Industries, Koç University, Rumelifeneri, Sarıyer, İstanbul, Turkey.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems2016In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, Västeraås, Sweden, 2016, Vol. 187, p. 107-112Conference 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.

  • 11.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kade, Daniel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, Christer
    Motion Control, Västerås, Sweden.
    Özcan, Oguzhan
    Koç University, Turkey.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    The effects of perceived USB-delay for sensor and embedded system development2016In: 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 (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.

1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf