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.