Deep Learning for Human Activity Recognition (eBook, PDF)

Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, Proceedings
Redaktion: Li, Xiaoli; Zhang, Le; Chen, Zhenghua; Wu, Min

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 139
• Erscheinungstermin: 17. Februar 2021
• Englisch
• ISBN-13: 9789811605758
• Artikelnr.: 62291481

Inhaltsangabe

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.