Behavior Analysis with Machine Learning Using R

Enrique Garcia Ceja

• Éditeur: Chapman and Hall/CRC
• Année: 2021
• ISBN:
  9781000484250
• Édition: 1
• Sujet: Psychology
• Sous-sujet: Research & Methodology in Psychology

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial.

Features:

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• Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on.

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• Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources.

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• Use unsupervised learning algorithms to discover criminal behavioral patterns.

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• Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images.

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• Evaluate the performance of your models in traditional and multi-user settings.

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• Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors.

This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

1Introduction to Behavior and Machine Learning

DOI: 10.1201/9781003203469-1

In the last years, machine learning has surged as one of the key technologies that enables and supports many of the services and products that we use in our everyday lives and is expanding quickly. Machine learning has also helped to accelerate research and development in almost every field including natural sciences, engineering, social sciences, medicine, art and culture. Even though all those fields (and their respective sub-fields) are very diverse, most of them have something in common: They involve living organisms (cells, microbes, plants, humans, animals, etc.) and living organisms express behaviors. This book teaches you machine learning and data-driven methods to analyze different types of behaviors. Some of those methods include supervised, unsupervised, and deep learning. You will also learn how to explore, encode, preprocess, and visualize behavioral data. While the examples in this book focus on behavior analysis, the methods and techniques can be applied in any other context.

This chapter starts by introducing the concepts of behavior and machine learning. Next, basic machine learning terminology is presented and you will build your first classification and regression models. Then, you will learn how to evaluate the performance of your models and important concepts such as underfitting, overfitting, bias, and variance.

1.1 What Is Behavior?

Living organisms are constantly sensing and analyzing their surrounding environment. This includes inanimate objects but also other living entities. All of this is with the objective of making decisions and taking actions, either consciously or unconsciously. If we see someone running, we will react differently depending on whether we are at a stadium or in a bank. At the same time, we may also analyze other cues such as the runner's facial expressions, clothes, items, and the reactions of the other people around us. Based on this aggregated information, we can decide how to react and behave. All this is supported by the organisms' sensing capabilities and decision-making processes (the brain and/or chemical reactions). Understanding our environment and how others behave is crucial for conducting our everyday life activities and provides support for other tasks. But, what is behavior? The Cambridge dictionary defines behavior as:

“the way that a person, an animal, a substance, etc. behaves in a particular situation or under particular conditions”.

Another definition by dictionary.com is:

“observable activity in a human or animal”.

The definitions are similar and both include humans and animals. Following those definitions, this book will focus on the automatic analysis of human and animal behaviors however, the methods can also be applied to robots and to a wide variety of problems in different domains. There are three main reasons why one may want to analyze behaviors in an automatic manner:

1. React. A biological or an artificial agent (or a combination of both) can take actions based on what is happening in the surrounding environment. For example, if suspicious behavior is detected in an airport, preventive actions can be triggered by security systems and the corresponding authorities. Without the possibility to automate such a detection system, it would be infeasible to implement it in practice. Just imagine trying to analyze airport traffic by hand.
2. Understand. Analyzing the behavior of an organism can help us to understand other associated behaviors and processes and to answer research questions. For example, Williams et al. [2020] found that Andean condors the heaviest soaring bird (see Figure 1.1), only flap their wings for about $1\%$ of their total flight time. In one of the cases, a condor flew $\approx 172$ km without flapping. Those findings were the result of analyzing the birds' behavior from data recorded by bio-logging devices. In this book, several examples that make use of inertial devices will be studied.
3. Document and Archive. Finally, we may want to document certain behaviors for future use. It could be for evidence purposes or maybe it is not clear how the information can be used now but may come in handy later. Based on the archived information, one could gain new knowledge in the future and use it to react (take decisions/actions), as shown in Figure 1.2. For example, we could document our nutritional habits (what do we eat, how often, etc.). If there is a health issue, a specialist could use this historical information to make a more precise diagnosis and propose actions.

FIGURE 1.1 Andean condor. (Hugo Pédel, France, Travail personnel. Cliché réalisé dans le Parc National Argentin Nahuel Huapi, San Carlos de Bariloche, Laguna Tonchek. Source: Wikipedia (CC BY-SA 3.0) [https://creativecommons.org/licenses/by-sa/3.0/legalcode]).

FIGURE 1.2 Taking decisions from archived behaviors.

Some behaviors can be used as a proxy to understand other behaviors, states, and/or processes. For example, detecting body movement behaviors during a job interview could serve as the basis to understand stress levels. Behaviors can also be modeled as a composition of lower-level behaviors. In chapter 7, a method called Bag of Words that can be used to decompose complex behaviors into a set of simpler ones will be presented.

In order to analyze and monitor behaviors, we need a way to observe them. Living organisms use their available senses such as eyesight, hearing, smell, echolocation (bats, dolphins), thermal senses (snakes, mosquitoes), etc. In the case of machines, they need sensors to accomplish or approximate those tasks, for example color and thermal cameras, microphones, temperature sensors, and so on.

The reduction in the size of sensors has allowed the development of more powerful wearable devices. Wearable devices are electronic devices that are worn by a user, usually as accessories or embedded in clothes. Examples of wearable devices are smartphones, smartwatches, fitness bracelets, actigraphy watches, etc. These devices have embedded sensors that allow them to monitor different aspects of a user such as activity levels, blood pressure, temperature, and location, to name a few. Examples of sensors that can be found in those devices are accelerometers, magnetometers, gyroscopes, heart rate, microphones, Wi-Fi, Bluetooth, Galvanic skin response (GSR), etc.

Several of those sensors were initially used for some specific purposes. For example, accelerometers in smartphones were intended to be used for gaming or detecting the device's orientation. Later, some people started to propose and implement new use cases such as activity recognition [Shoaib et al., 2015] and fall detection. The magnetometer, which measures the earth's magnetic field, was mainly used w...