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001444465 001__ 1444465
001444465 003__ OCoLC
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001444465 019__ $$a1296531274$$a1296582858
001444465 020__ $$a9783030955939$$q(electronic bk.)
001444465 020__ $$a3030955931$$q(electronic bk.)
001444465 020__ $$z9783030955922$$q(print)
001444465 020__ $$z3030955923
001444465 0247_ $$a10.1007/978-3-030-95593-9$$2doi
001444465 035__ $$aSP(OCoLC)1297831039
001444465 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCO$$dOCLCF$$dOCLCQ
001444465 049__ $$aISEA
001444465 050_4 $$aTK5103.35
001444465 08204 $$a004.6/8$$223
001444465 1112_ $$aEAI International Conference on Body Area Networks$$n(16th :$$d2021 :$$cOnline)
001444465 24510 $$aBody area networks :$$bsmart IoT and big data for intelligent health management : 16th EAI International Conference, BODYNETS 2021, Virtual event, October 25-26, 2021, Proceedings /$$cMasood Ur Rehman, Ahmed Zoha (eds.).
001444465 2463_ $$aBODYNETS 2021
001444465 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001444465 300__ $$a1 online resource (x, 322 pages) :$$billustrations (some color).
001444465 336__ $$atext$$btxt$$2rdacontent
001444465 337__ $$acomputer$$bc$$2rdamedia
001444465 338__ $$aonline resource$$bcr$$2rdacarrier
001444465 4901_ $$aLecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,$$x1867-822X ;$$v420
001444465 500__ $$aIncludes author index.
001444465 5050_ $$aIntro -- Preface -- Conference Organization -- Contents -- Human Activity Recognition -- Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor -- 1 Introduction -- 2 Materials and Methods -- 2.1 IMU State Modeling -- 2.2 USRP State Modeling -- 3 The Proposed Structure Matrix to Data Fusion -- 3.1 Principal Component Analysis for Feature Extraction -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Indoor Activity Position and Direction Detection Using Software Defined Radios -- 1 Introduction -- 2 Materials and Methods -- 2.1 Technical Specifications
001444465 5058_ $$a2.2 Experimental Design -- 3 Results and Discussion -- 3.1 Detection Accuracy vs. Activity Position -- 3.2 Detecting Position, Direction of Movement, and Occupancy -- 4 Conclusion -- References -- Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8GHz Frequency Modulated Continuous Wave Radar and ResNet Algorithm -- 1 Introduction -- 2 Methodology -- 2.1 Data Acquisition -- 2.2 Classification Using Residual Neural Network -- 3 Results and Discussion -- 4 Conclusions and Future Work -- References
001444465 5058_ $$aElderly Care -- Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps -- 1 Introduction -- 1.1 Context -- 1.2 Current Research Progress -- 2 Methodology and Implementation -- 2.1 Dataset Information -- 2.2 Pre-processing -- 2.3 Feature Extraction and Classification -- 3 Results and Discussion -- 3.1 Hardware and Software Environment -- 3.2 Classification Results -- 3.3 Discussion -- 4 Conclusions and Future Work -- References -- Wireless Sensing for Human Activity Recognition Using USRP -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection
001444465 5058_ $$a3.2 Machine Learning -- 4 Results and Discussion -- 4.1 Machine Learning Algorithms Comparison -- 4.2 Real Time Classification -- 4.3 Benchmark Dataset -- 5 Conclusion -- References -- Real-Time People Counting Using IR-UWB Radar -- 1 Introduction -- 2 Methodology -- 2.1 People Counting Algorithm -- 2.2 Experiment -- 3 Results -- 4 Conclusion -- References -- Bespoke Simulator for Human Activity Classification with Bistatic Radar -- 1 Introduction -- 2 Radar Simulation -- 3 Classification -- 3.1 Feature Extraction -- 3.2 Classification Algorithm -- 4 Classification Results
001444465 5058_ $$a4.1 Monostatic Results -- 4.2 Bistatic Results -- 5 Discussion -- 5.1 Monostatic -- 5.2 Bistatic -- 6 Conclusion -- References -- Sensing for Healthcare -- Detecting Alzheimer's Disease Using Machine Learning Methods -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Machine Learning Methods -- 3.2 Deep Learning Methods -- 4 Experimental Results and Discussions -- 4.1 Discussion -- 5 Conclusion -- References -- FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patients -- 1 Introduction -- 2 Related Work -- 2.1 Overview of Recent Methods of Detecting FoG
001444465 506__ $$aAccess limited to authorized users.
001444465 520__ $$aThis book constitutes the refereed post-conference proceedings of the 16th International Conference on Body Area Networks, BodyNets 2021, held in October 2021. The conference was held virtually due to the COVID-19 pandemic. The 21 papers presented were selected from 44 submissions and issue new technologies to provide trustable measuring and communications mechanisms from the data source to medical health databases. Wireless body area networks (WBAN) are one major element in this process. Not only on-body devices but also technologies providing information from inside a body are in the focus of this conference. Dependable communications combined with accurate localization and behavior analysis will benefit WBAN technology and make the healthcare processes more effective.
001444465 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 17, 2022).
001444465 650_0 $$aBody area networks (Electronics)$$vCongresses.
001444465 650_6 $$aRéseaux corporels (Électronique)$$vCongrès.
001444465 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001444465 655_0 $$aElectronic books.
001444465 7001_ $$aUr-Rehman, Masood,$$eeditor.$$1https://orcid.org/0000-0001-6926-7983
001444465 7001_ $$aZoha, Ahmed,$$eeditor.$$0(orcid)0000-0001-7497-9336$$1https://orcid.org/0000-0001-7497-9336
001444465 77608 $$iPrint version: $$z3030955923$$z9783030955922$$w(OCoLC)1290430897
001444465 830_0 $$aLecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering ;$$v420.$$x1867-822X
001444465 852__ $$bebk
001444465 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-95593-9$$zOnline Access$$91397441.1
001444465 909CO $$ooai:library.usi.edu:1444465$$pGLOBAL_SET
001444465 980__ $$aBIB
001444465 980__ $$aEBOOK
001444465 982__ $$aEbook
001444465 983__ $$aOnline
001444465 994__ $$a92$$bISE