The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. Sensors and cameras are exploited for the analysis and recognition of human activity and behavior. In the book Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors, we have divided across two volumes, 40 wonderful chapters under five parts: Part 1: Healthcare and Emotion (Chapters 1-7), Part 2: Mental Health (Chapters 8-14), Part 3: Nurse Care Records (Chapters 15-26), Part 4: Movement and Sensors (Chapters 27-36), and Part 5: Sports Activity Analysis (Chapters 37-40). In a complex problem such as stress detection, the application of some type of Machine Learning (ML) algorithms makes sense. The vast amount of data in a context where multiple variables, such as HR, HRV, GSR, and ST, might have different outputs based on each other, makes it a prime target for the ML approach. Some of the most common classification algorithms are Support Vector Machines (SVM), K-Nearest Neighbours (K-NN), Random Forests (RF), Decision Trees, and Naive Bayes (NB). The data processing and model development used different Python libraries such as Pandas, Tensorflow, and Keras. To recognize stress from the physiological data, we tested different Machine Learning algorithms. We implemented the approach using Python and the Scikit-learn library.

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