Chapter 1. Introduction
Chapter 2. Activities
Definitions
Classes of Activities
Additional Reading
Chapter 3. Sensing
Sensors Used for Activity Learning
Sample Sensor Datasets
Features
Multisensor Fusion
Additional Reading
Chapter 4. Machine Learning
Supervised Learning Framework
Naive Bayes Classifier
Gaussian Mixture Model
Hidden Markov Model
Decision Tree
Support Vector Machine
Conditional Random Field
Combining Classifier Models
Dimensionality Reduction
Additional Reading
Chapter 5. Activity Recognition
Activity Segmentation
Sliding Windows
Unsupervised Segmentation
Measuring Performance
Additional Reading
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Chapter 6. Activity Discovery
Zero-Shot Learning
Sequence Mining
Clustering
Topic Models
Measuring Performance
Additional Reading
Chapter 7. Activity Prediction
Activity Sequence Prediction
Activity Forecasting
Probabilistic Graph-Based Activity Prediction
Rule-Based Activity Timing Prediction
Measuring Performance
Additional Reading
Chapter 8. Activity Learning in the Wild
Collecting Annotated Sensor Data
Transfer Learning
Multi-Label Learning
Activity Learning for Multiple Individuals
Additional Reading
Chapter 9. Applications of Activity Learning
Health
Activity-Aware Services
Security and Emergency Management
Activity Reconstruction, Expression and Visualization
Analyzing Human Dynamics
Additional Reading
Chapter 10. The Future of Activity Learning
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