Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects. Activity recognition has been a hot research topic for some years now. One of its main applications is ambient assisted living, so that people in need of assistance might stay in their own environment longer than previously possible. There is a wide range of sensors of different types and applications being tested in this domain, ranging from ambient binary motion sensors and pressure mats to wearable gyroscopes or sensors monitoring body functions. This is also due to the many different levels of surveillance tested by the researchers. The library we selected to use here is Apache Beam and in-memory database Redis. There are two reasons to use Apache Beam: The first is the ease of implementation. Apache Beam can be written in programming languages such as Java or Python, and the process is represented as a pipeline, so it is intuitive and easy to implement. We used Machine Learning methods for classification and extracted statistical features from raw 3-D acceleration and 3-D angular velocity for each axis within the segment. In this work, we compared three types of classifiers, multi-class support vector machine (SVM), Random Forest (RF), and K Nearest Neighbors (KNN) from Scikit-learn, a Machine Learning library for Python. We chose to set all of the parameters of Machine Learning models to default values of Scikit-learn. Before training data with classifiers, we chose RF and KNN classifiers which calculate distances between different points in their algorithm.

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