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Toward Complex 3D Movement Detection to Analyze Human Behavior via Radio-Frequency Signals

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Michelson, David, Assoc. Prof., University of British Columbia, Canada
dc.contributor.author Raja, Muneeba
dc.date.accessioned 2020-08-29T09:00:09Z
dc.date.available 2020-08-29T09:00:09Z
dc.date.issued 2020
dc.identifier.isbn 978-952-60-3988-6 (electronic)
dc.identifier.isbn 978-952-60-3986-2 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/46311
dc.description A doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Electrical Engineering, at a public examination held with remote technology on 15 September 2020 at 6:00pm. Zoom link: https://aalto.zoom.us/j/6549167529
dc.description.abstract A driver's attention, parallel actions, and emotions directly influence driving behavior. Any secondary task, be it cognitive, visual, or manual, that diverts driver focus from the primary task of driving is a source of distraction. Longer response time, inability to scan the road, and missing visual cues can all lead to car crashes with serious consequences. Current research focuses on detecting distraction by means of vehicle-mounted video cameras or wearable sensors for tracking eye movements and head rotation. Facial expressions, speech, and physiological signals are also among the widely used indicators for detecting distraction. These approaches are accurate, fast, and reliable but come with a high installation cost, requirements related to lighting conditions, privacy intrusions, and energy consumption. Over the past decade, the use of radio signals has been investigated as a possible solution for the aforementioned limitations of today's technologies. Changes in radio-signal patterns caused by movements of the human body can be analyzed and thereby used in detecting humans' gestures and activities. Human behavior and emotions, in particular, are less explored in this regard and are addressed mostly with reference to physiological signals. The thesis exploited multiple wireless technologies (1.8~GHz, WiFi, and millimeter wave) and combinations thereof to detect complex 3D movements of a driver in a car. Upper-body movements are vital indicators of a driver's behavior in a car, and the information from these movements could be used to generate appropriate feedback, such as warnings or provision of directives for actions that would avoid jeopardizing safety. Existing wireless-system-based solutions focus primarily on either large or small movements, or they address well-defined activities. They do not consider discriminating large movements from small ones, let alone their directions, within a single system. These limitations underscore the requirement to address complex natural-behavior situations precisely such as that in a car, which demands not only isolating particular movements but also classifying and predicting them. The research to reach the attendant goals exploited physical properties of RF signals, several hardware-software combinations, and building of algorithms to process and detect body movements -- from the simple to the complex. Additionally, distinctive feature sets were addressed for machine-learning techniques to find patterns in data and predict states accordingly. The systems were evaluated by performing extensive real-world studies. en
dc.format.extent 131 + app. 101
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 117/2020
dc.relation.haspart [Publication 1]: Sanaz Kianoush, Muneeba Raja, Stefano Savazzi, Stephan Sigg. A cloud- IoT platform for passive radio sensing: Challenges and application case studies. IEEE Internet of Things Journal, Volume 5, No. 5, pages 3624– 3636, 2018 May. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201901301425. DOI: 10.1109/JIOT.2018.2834530
dc.relation.haspart [Publication 2]: Muneeba Raja, Stephan Sigg. Applicability of RF-based methods for emotion recognition: A survey. In IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia, Mar. 2016. DOI: 10.1109/PERCOMW.2016.7457119
dc.relation.haspart [Publication 3]: Muneeba Raja, Anja Exler, Samuli Hemminki, Shinichi Konomi, Stephan Sigg, Sozo Inoue. Towards pervasive geospatial affect perception. GeoInformatica, Volume 22, No. 1, pages 143–169, Jan. 2018. DOI: 10.1007/s10707-017-0294-1
dc.relation.haspart [Publication 4]: Muneeba Raja, Stephan Sigg. RFexpress! Exploiting the wireless network edge for RF-based emotion sensing. In 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Cyprus, Sept. 2017. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201803231779. DOI: 10.1109/PERCOMW.2017.7917516
dc.relation.haspart [Publication 5]: Muneeba Raja, Viviane Ghaderi, Stephan Sigg. WiBot! In-vehicle behaviour and gesture recognition using wireless network edge. In IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, pages 376–387, July 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201812075894. DOI: 10.1109/ICDCS.2018.00045
dc.relation.haspart [Publication 6]: Muneeba Raja, Aidan Hughes, Yixuan Xu, Parham Zarei, David Michelson, Stephan Sigg. Wireless multi-frequency feature set to simplify human 3D pose estimation. IEEE Antennas and Wireless Propagation Letters, Volume 18, No. 5, pages 876–880, Mar. 2019. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201906033466. DOI: 10.1109/LAWP.2019.2904580
dc.relation.haspart [Publication 7]: Muneeba Raja, Zahra Vali, Sameera Palipana, David Michelson, Stephan Sigg. 3D head motion detection using millimeter-wave Doppler radar. IEEE Access, Volume 8, pages 32321–32331, Feb. 2020. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202004092804. DOI: 10.1109/ACCESS.2020.2973957
dc.subject.other Communication en
dc.title Toward Complex 3D Movement Detection to Analyze Human Behavior via Radio-Frequency Signals en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.contributor.school School of Electrical Engineering en
dc.contributor.department Tietoliikenne- ja tietoverkkotekniikan laitos fi
dc.contributor.department Department of Communications and Networking en
dc.subject.keyword wireless sensing en
dc.subject.keyword body movement detection en
dc.subject.keyword behavior recognition en
dc.subject.keyword distracted state detection en
dc.identifier.urn URN:ISBN:978-952-60-3988-6
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Sigg, Stephan, Assoc. Prof., Aalto University, Department of Communications and Networking, Finland
dc.opn Pätzold, Matthias, Prof., University of Agder, Norway
dc.contributor.lab Aalto Ambient Intelligence en
dc.rev Li, Mo, Assoc. Prof., Nanyang Technological University, Singapore
dc.rev Adib, Fadel, Asst. Prof., Massachusetts Institute of Technology, USA
dc.date.defence 2020-09-15
local.aalto.acrisexportstatus checked 2020-10-19_1140
local.aalto.formfolder 2020_08_28_klo_15_07
local.aalto.archive yes

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