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The effects of perceived USB-delay for sensor and embedded system development
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4947-5037
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7722-5310
Motion Control, Västerås, Sweden.
Koç University, Turkey.ORCID iD: 0000-0001-8828-1684
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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.

Place, publisher, year, edition, pages
2016. p. 2492-2495, article id 7591236
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-32878DOI: 10.1109/EMBC.2016.7591236ISI: 000399823502209Scopus ID: 2-s2.0-85009097778ISBN: 9781457702204 (print)OAI: oai:DiVA.org:mdh-32878DiVA, id: diva2:956346
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 ProfileAvailable from: 2016-08-30 Created: 2016-08-24 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: 2019-02-19Bibliographically approved

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Du, JiayingKade, DanielÖzcan, OguzhanLindén, Maria

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