Deep Learning for Human Activity Recognition de Xiaoli Li

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: Communications in Computer and Information Science, cartea 1370

Xiaoli Li

Min Wu

Zhenghua Chen

Le Zhang

en Limba Engleză Paperback – 18 feb 2021

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.

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Specificații

ISBN-13: 9789811605741
ISBN-10: 9811605742
Pagini: 139
Ilustrații: XII, 139 p. 51 illus., 49 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.22 kg
Ediția:1st ed. 2021
Editura: Springer Nature Singapore
Colecția Springer
Seria Communications in Computer and Information Science

Locul publicării:Singapore, Singapore

Cuprins

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.