Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

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.

Avainsanat: Sensor; Multisensor Fusion; Na?ve Bayes Classifier; Markov Model; Zero-Shot Learning; Co-occurrence Data; Passive Infrared (PIR) Sensors; Radi-frequency Identification (RFID) Sensors; Global Positioning System (GPS) Sensors; Gyroscope; Gaussian Mixture Model; Hidden Markov Module; Principal Control Analysis (PCA) Algorithm; Principal Control Analysis (PCA) Dimensionality Reduction; Kappa Statistic; Receiving Operating Characteristic Curve; Dunn Index; Davies-Bouldin Index; Active LeZi Algorithm; Jaccard Index; Kullback-Leibler; Manifold Alignment; Damerau-Levenshtein Distance, Pattern Analysis, Sensors, Instrumentation & Measurement, Pattern Analysis, Sensors, Instrumentation & Measurement