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Deep Learning for Human Activity Recognition: Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, Proceedings 1st ed. 2021 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 139 pages, height x width: 235x155 mm, weight: 454 g, 49 Illustrations, color; 2 Illustrations, black and white; XII, 139 p. 51 illus., 49 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1370
  • Izdošanas datums: 18-Feb-2021
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811605742
  • ISBN-13: 9789811605741
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Deep Learning for Human Activity Recognition: Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, Proceedings 1st ed. 2021Palielināt
  • Formāts: Paperback / softback, 139 pages, height x width: 235x155 mm, weight: 454 g, 49 Illustrations, color; 2 Illustrations, black and white; XII, 139 p. 51 illus., 49 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1370
  • Izdošanas datums: 18-Feb-2021
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811605742
  • ISBN-13: 9789811605741
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789811605741.html
This 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. 
Human 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.- Efcacy 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.