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020 _a9783030130015
_9978-3-030-13001-5
024 7 _a10.1007/978-3-030-13001-5
_2doi
050 4 _aQA76.9.U83
050 4 _aQA76.9.H85
072 7 _aUYZ
_2bicssc
072 7 _aCOM079010
_2bisacsh
072 7 _aUYZ
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082 0 4 _a005.437
_223
082 0 4 _a004.019
_223
245 1 0 _aHuman Activity Sensing
_h[electronic resource] :
_bCorpus and Applications /
_cedited by Nobuo Kawaguchi, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, Kristof Van Laerhoven.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXII, 250 p. 140 illus., 98 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Adaptive Environments,
_x2522-5537
505 0 _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.
520 _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.
650 0 _aUser interfaces (Computer systems).
650 0 _aHuman-computer interaction.
650 0 _aData mining.
650 0 _aApplication software.
650 0 _aMicroprogramming .
650 1 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aControl Structures and Microprogramming.
700 1 _aKawaguchi, Nobuo.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aNishio, Nobuhiko.
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_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRoggen, Daniel.
_eeditor.
_4edt
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700 1 _aInoue, Sozo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPirttikangas, Susanna.
_eeditor.
_4edt
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700 1 _aVan Laerhoven, Kristof.
_eeditor.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030130008
776 0 8 _iPrinted edition:
_z9783030130022
776 0 8 _iPrinted edition:
_z9783030130039
830 0 _aSpringer Series in Adaptive Environments,
_x2522-5537
856 4 0 _uhttps://doi.org/10.1007/978-3-030-13001-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173194
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