Description
The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).
Table of Contents
Chapter 1. Summary of the Cooking Activity Recognition Challenge.- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN.- Chapter 3. Let’s not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset.- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data.- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data.- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge.- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition.- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote.- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge.- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework.
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