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  • Produktbild: Deep Learning for Human Activity Recognition
  • Produktbild: Deep Learning for Human Activity Recognition
Band 1370

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

Aus der Reihe Communications in Computer and Information Science

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Beschreibung

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...

Beschreibung

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. 

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

18.02.2021
Abbildungen

XII, 139 p. 51 illus., 49 illus. in color.
Herausgeber Xiaoli Li + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

18.02.2021
Abbildungen

XII, 139 p. 51 illus., 49 illus. in color.
Herausgeber
Verlag Springer Singapore
Seitenzahl

139
Maße (L/B/H)

23,5/15,5/0,9 cm
Gewicht

242 g
Auflage

1st ed. 2021
Sprache

Englisch
ISBN

978-981-16-0574-1
Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Deep Learning for Human Activity Recognition
  • Produktbild: Deep Learning for Human Activity Recognition
  • 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.- Efficacy 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.