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

Author:   Xiaoli Li ,  Min Wu ,  Zhenghua Chen ,  Le Zhang
Publisher:   Springer Verlag, Singapore
Edition:   1st ed. 2021
Volume:   1370
ISBN:  

9789811605741


Pages:   139
Publication Date:   18 February 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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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


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Overview

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. 

Full Product Details

Author:   Xiaoli Li ,  Min Wu ,  Zhenghua Chen ,  Le Zhang
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2021
Volume:   1370
Weight:   0.454kg
ISBN:  

9789811605741


ISBN 10:   9811605742
Pages:   139
Publication Date:   18 February 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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