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Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-4947-5037
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Motion Control i Västerås AB, Västerås, Sweden.ORCID-id: 0000-0001-7722-5310
Motion Control i Västerås AB, Västerås, Sweden.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-3163-6039
Vise andre og tillknytning
2016 (engelsk)Inngår i: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, Västeraås, Sweden, 2016, Vol. 187, s. 107-112Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Västeraås, Sweden, 2016. Vol. 187, s. 107-112
Serie
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Emneord [en]
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
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-33810DOI: 10.1007/978-3-319-51234-1_17ISI: 000428954100017Scopus ID: 2-s2.0-85011301202OAI: oai:DiVA.org:mdh-33810DiVA, id: diva2:1048572
Konferanse
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västeraås, Sweden
Prosjekter
ITS-EASY Post Graduate School for Embedded Software and SystemsESS-H - Embedded Sensor Systems for Health Research ProfileTilgjengelig fra: 2016-11-21 Laget: 2016-11-21 Sist oppdatert: 2019-02-08bibliografisk kontrollert
Inngår i avhandling
1. Real-time signal processing in MEMS sensor-based motion analysis systems
Åpne denne publikasjonen i ny fane eller vindu >>Real-time signal processing in MEMS sensor-based motion analysis systems
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2019
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 285
HSV kategori
Forskningsprogram
elektronik
Identifikatorer
urn:nbn:se:mdh:diva-42619 (URN)978-91-7485-421-3 (ISBN)
Disputas
2019-03-19, Gamma, Mälardalens högskola, Västerås, 09:30 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-02-08 Laget: 2019-02-08 Sist oppdatert: 2019-09-20bibliografisk kontrollert

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