Human Activity Recognition Challenge 9811582688, 9789811582684

Table of contents :
Preface
Contents
Editors and Contributors
Summary of the Cooking Activity Recognition Challenge
1 Introduction
2 Dataset Description
2.1 Activities Collected
2.2 Experimental Settings and Sensor Modalities
2.3 Data Format
3 Challenge Tasks and Results
3.1 Evaluation Metric
3.2 Results
4 Conclusion
References
Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN
1 Introduction
2 Related Work
3 Method
3.1 GCN for Micro-Activity Recognition
3.2 CNN for Macro-Activity Recognition
4 Experimental Results
4.1 Dataset
4.2 Data Processing
4.3 Results
5 Conclusions
References
Let's Not Make It Complicated—Using Only LightGBM and Naive Bayes for Macro- and Micro-Activity Recognition from a Small Dataset
1 Introduction
2 Challenge and Dataset Details
3 Preprocessing and Feature Engineering
4 Classification Model
5 Experimental Results and Discussion
6 Background and Related Works
7 Conclusion
References
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data
1 Introduction
2 Challenges and Training Pipeline
2.1 Handling Missing Data
2.2 Jitter and Data Augmentation
3 Implemented Deep Learning Architectures
3.1 Convolutional Neural Networks
3.2 LSTM Recurrent Neural Network
3.3 Deep Convolutional Bidirectional LSTM
3.4 Implementation
4 Classification Results
5 Analysis and Discussion
6 Conclusion and Future Work
References
SCAR-Net: Scalable ConvNet for Activity Recognition with Multimodal Sensor Data
1 Introduction
2 Methodology and Result Analysis
2.1 Contribution
2.2 Macro-Activity Recognition
2.3 Micro-Activity Recognition
3 Conclusion
References
Multi-sampling Classifiers for the Cooking Activity Recognition Challenge
1 Introduction
2 Related Work
2.1 Learning Subclass Representations for Visually Varied Image Classification
3 Multi-sampling Classifiers
3.1 Multi-classifiers
3.2 Multi-sampling
4 Experimental Results and Discussion
5 Conclusion
References
Multi-class Multi-label Classification for Cooking Activity Recognition
1 Introduction
2 Related Work
3 Dataset
4 Cooking Activity Recognition Pipeline
5 Results and Discussion
5.1 Macro- and Micro-Activity Recognition Results
5.2 Interpretation of Cooking Activity Features
5.3 Performance with Different Imputation Strategies
5.4 Single-Sensor and Multi-Sensor Feature Performance
6 Limitations and Future Work
7 Conclusions
References
Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote
1 Introduction
2 Challenge
2.1 Challenge Goal
2.2 Dataset
2.3 Evaluation Criteria
3 Method
3.1 Preprocessing
3.2 Model
3.3 Loss Function and Optimizer
3.4 Final Prediction Classes Activation
4 Evaluation
4.1 Environment
4.2 Result
5 Conclusion
6 Appendix
6.1 Used Sensor Modalities
6.2 Features Used
6.3 Programming Language and Libraries Used
6.4 Window Size and Post-processing
6.5 Training and Testing Time
6.6 Machine Specification
References
Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge
1 Introduction
1.1 Method Overview
2 Challenge Data
2.1 Data Preprocessing
3 Feature Extraction
4 Classification
4.1 Classifiers
4.2 Post-Processing with a Hidden Markov Model
5 Results
6 Discussion
7 Conclusion
8 Appendix: General Information
References
Cooking Activity Recognition with Varying Sampling Rates Using Deep Convolutional GRU Framework
1 Introduction
2 Related Works
3 Dataset Description
4 Proposed Methodology
4.1 Preprocessing and Segmentation
4.2 Feature Extraction
4.3 Framework Architecture
5 Results and Discussions
5.1 Macro-Activity Evaluation
5.2 Micro-Activity Evaluation
6 Conclusion
References

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