LEADER 03001nam 2200457 450 001 996464505103316 005 20210330193828.0 010 $a981-16-0575-0 024 7 $a10.1007/978-981-16-0575-8 035 $a(CKB)4100000011772805 035 $a(DE-He213)978-981-16-0575-8 035 $a(MiAaPQ)EBC6480824 035 $a(PPN)253859522 035 $a(EXLCZ)994100000011772805 100 $a20210330d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDeep learning for human activity recognition $eSecond International Workshop, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, proceedings /$fXiaoli Li [and three others] (editors) 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XII, 139 p. 51 illus., 49 illus. in color.) 225 1 $aCommunications in computer and information science ;$v1370 311 $a981-16-0574-2 327 $aHuman Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark -- Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks -- Toward Data Augmentation and Interpretation in Sensor-Based Fine-Grained Hand Activity Recognition -- Personalization Models for Human Activity Recognition With Distribution Matching-Based Metrics -- Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition -- ARID: A New Dataset for Recognizing Action in the Dark -- Single Run Action Detector over Video Stream - A Privacy Preserving Approach -- Ef?cacy of Model Fine-Tuning for Personalized Dynamic Gesture Recognition -- Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes -- Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network. 330 $aThis book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more. . 410 0$aCommunications in computer and information science ;$v1370. 606 $aMachine learning$vCongresses 615 0$aMachine learning 676 $a006.31 702 $aLi$b Xiao-Li$f1969- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464505103316 996 $aDeep learning for human activity recognition$92814148 997 $aUNISA