Diane J. Cook, Narayanan C. Krishnan

Activity Learning (eBook, ePUB)

Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

• Format: ePub

Produktbeschreibung

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: * Discovering activity patterns that emerge from behavior-based sensor data * Recognizing occurrences of predefined or discovered activities in real time * Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 288
• Erscheinungstermin: 19. Februar 2015
• Englisch
• ISBN-13: 9781119010241
• Artikelnr.: 42369385

Autorenporträt

DIANE J. COOK, PhD, is a professor in the School of Electrical Engineering and Computer Science at Washington State University, USA. Her research relating to artificial intelligence and data mining have been supported by grants from the National Science Foundation, the National Institutes of Health, NASA, DARPA, USAF, NRL, and DHS. She is the co-author of Mining Graph Data and Smart Environments, both published by Wiley. Dr. Cook is an IEEE fellow and a member of AAAI. NARAYANAN C. KRISHNAN, PhD, is a faculty member of the Department of Computer Science and Engineering at the Indian Institute of Technology Ropar, India. His research focuses on activity recognition, pervasive computing, and applied machine learning. Dr. Krishnan received the gold medal for academic excellence in Masters of Technology in Computer Science in 2004 and was nominated for the Best PhD Thesis Award at Arizona State University in 2010.

Inhaltsangabe

1 Introduction

2 Activities

2.1 Definitions

2.2 Classes of Activities

2.3 Additional Reading

3 Sensing

3.1 Sensors Used for Activity Learning

3.2 Sample Sensor Datasets

3.3 Features

3.4 Multisensor Fusion

3.5 Additional Reading

4 Machine Learning

4.1 Supervised Learning Framework

4.2 Naïve Bayes Classifier

4.3 Gaussian Mixture Model

4.4 Hidden Markov Model

4.5 Decision Tree

4.6 Support Vector Machine

4.7 Conditional Random Field

4.8 Combining Classifier Models

4.9 Dimensionality Reduction

4.10 Additional Reading

5 Activity Recognition

5.1 Activity Segmentation

5.2 Sliding Windows

5.3 Unsupervised Segmentation

5.4 Measuring Performance

5.5 Additional Reading

6 Activity Discovery

6.1 Zero-Shot Learning

6.2 Sequence Mining

6.3 Clustering

6.4 Topic Models

6.5 Measuring Performance

6.6 Additional Reading

7 Activity Prediction

7.1 Activity Sequence Prediction

7.2 Activity Forecasting

7.3 Probabilistic Graph-Based Activity Prediction

7.4 Rule-Based Activity Timing Prediction

7.5 Measuring Performance

7.6 Additional Reading

8 Activity Learning in the Wild

8.1 Collecting Annotated Sensor Data

8.2 Transfer Learning

8.3 Multi-Label Learning

8.4 Activity Learning for Multiple Individuals

8.5 Additional Reading

9 Applications of Activity Learning

9.1 Health

9.2 Activity-Aware Services

9.3 Security and Emergency Management

9.4 Activity Reconstruction, Expression and Visualization

9.5 Analyzing Human Dynamics

9.6 Additional Reading

10 The Future of Activity Learning

Appendix: Sample Activity Data

Bibliography