LEADER 04463nam 22006735 450 001 9910349283403321 005 20200705131146.0 010 $a3-030-13001-0 024 7 $a10.1007/978-3-030-13001-5 035 $a(CKB)4100000009191111 035 $a(DE-He213)978-3-030-13001-5 035 $a(MiAaPQ)EBC5894495 035 $a(PPN)258306432 035 $a(EXLCZ)994100000009191111 100 $a20190909d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHuman Activity Sensing $eCorpus and Applications /$fedited by Nobuo Kawaguchi, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, Kristof Van Laerhoven 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XII, 250 p. 140 illus., 98 illus. in color.) 225 1 $aSpringer Series in Adaptive Environments,$x2522-5529 311 $a3-030-13000-2 320 $aIncludes bibliographical references. 327 $aOptimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording -- Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data -- Compensation Scheme for PDR using Component-wise Error Models -- Towards the Design and Evaluation of Robust Audio-Sensing Systems -- A Wi-Fi Positioning Method Considering Radio Attenuation of Human Body -- Drinking gesture recognition from poorly annotated data: a case study -- Understanding how Non-experts Collect and Annotate Activity Data -- MEASURed: Evaluating Sensor-based Activity Recognition Scenarios by Simulating Accelerometer Measures from Motion Capture -- Benchmark performance for the Sussex-Huawei locomotion and transportation recognition challenge 2018 -- Effects of Activity Recognition Window Size and Time Stabilization in the SHL Recognition Challenge. 330 $aActivity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users. 410 0$aSpringer Series in Adaptive Environments,$x2522-5529 606 $aUser interfaces (Computer systems) 606 $aData mining 606 $aApplication software 606 $aMicroprogramming  606 $aUser Interfaces and Human Computer Interaction$3https://scigraph.springernature.com/ontologies/product-market-codes/I18067 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aControl Structures and Microprogramming$3https://scigraph.springernature.com/ontologies/product-market-codes/I12018 615 0$aUser interfaces (Computer systems). 615 0$aData mining. 615 0$aApplication software. 615 0$aMicroprogramming . 615 14$aUser Interfaces and Human Computer Interaction. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aControl Structures and Microprogramming. 676 $a005.437 676 $a4.019 676 $a006.25 702 $aKawaguchi$b Nobuo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNishio$b Nobuhiko$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRoggen$b Daniel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aInoue$b Sozo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPirttikangas$b Susanna$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVan Laerhoven$b Kristof$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910349283403321 996 $aHuman Activity Sensing$92533674 997 $aUNINA