Deep Learning for Human Activity Recognition: Second International Workshop, DL-Har 2020, Held in Conjunction with Ijcai-Pricai 2020, Kyoto, Japan, Ja

ISBN-13: 9789811605741 / Angielski / Miękka / 2021 / 139 str.

Xiaoli Li; Min Wu; Zhenghua Chen

Spis treści

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.