# Activity Prediction Based on Deep Learning Techniques
## 1 Introduction  
Human activity and behavior recognition (HABR) is an area of research to gain a high level of knowledge about human activities from raw sensor inputs, and it is regarded as an essential component to realize the 4 th industrial revolution. Technologies of the 4 th Industrial Revolution society will primarily comprise IoT sensors broadly deployed almost everywhere and intelligent software technologies such as artificial intelligence, machine learning, and so on. These technologies are collectively used to make our daily lives better by making various useful applications viable, which include healthcare, surveillance, location-based services, silver-care services, etc. Let alone several of these interesting applications, environmental issues, including the air pollution problem, have drawn lots of attention, especially in most Asian countries where the economy is growing very rapidly. Many countries have made efforts to develop technologies to predict the level and impact of air pollution in our daily life so that people can avoid air pollution and protect themselves. Related research efforts have focused on predicting the level of air pollution based on data collected from the sensors deployed broadly with the aim of alerting the untargeted majority of people. This kind of service needs to be advanced to provide a personal care type of service so that each individual can have different environmental information. If this kind of service is desired, it is essential to track the precise movement of individuals so that it is possible to let people know of their level of pollution in advance. Thus, it is important to predict the activities or behaviors of humans in a very reliable manner. This paper strived to develop deep learning prediction models to predict the activities of humans, using datasets collected from body-worn sensors designed to obtain personal environmental data during daily life. Related studies have presented online and offline predictive technologies for human activities, primarily in outdoor or indoor environments. However, our daily lifeis composed of lots of diverse indoor and outdoor activities; therefore, research efforts should develop into working in any environment afterward. In this study, we developed two machine-learning-based activity prediction models applicable to our daily lives and evaluated the performance of the models to weigh up the possibility of their practical use. Lots of technologies have been proposed to predict human behaviors and activities. Most of them are classified into two different categories depending on the type of data collected [1]. We can predict human behaviors using image or video data (image-based technologies) or sensor data (sensor-type technologies) collected from mobile devices or from stationary sensors deployed at home or in any target area. For the literature review, we excluded image-based studies and focused primarily on those using non-image data, collected especially from mobile devices. Sensor data can be classified into two different categories, mobile or stationary. Mobile-type sensors are, in general, worn by the subject person and generate data associated with the movements of the subject person, so it is highly likely to contain a significant amount of missing data or noise caused by the movements [2; 3; 4; 5]. Stationary sensors are, in general, installed in places of interest, and the collected data contains relatively fewer missing values and noise compared to those of mobiles [6; 7; 8; 9; 10]. It is also important to differentiate whether the activity recognition is performed in online or offline environments. Online recognition, in general, refers to approaches in which the recognition tasks occur mostly in local devices and are executed in real time. Offline recognition, on the other hand, works in client-server computing environments and does not require real-time processing. Our designed classifiers are developed to apply to both environments and to work online as well as offline.