# Correlation between Human Emotion and Temporal-Spatial Contexts by Analyzing Environmental Factors
## 1 Introduction  
The recent proliferation of smartphones and wearable devices, coupled with advances in information and communication technologies, has resulted in an unprecedented increase in the scope and amount of personal information collected on a daily basis. Moreover, technology is becoming increasingly important for the efficient management of data and information, which is a very challenging task in contemporary society. Lifelogging technology is particularly useful because it facilitates the extraction of meaningful information from data generated during everyday life experiences, as well as information from a wide variety of fields. Lifelogging essentially refers to a \"life record\", derived from a combination of the words \"life\" and \"log\". The term refers to using technology in the recording, saving, and organizing of events that an individual might experience [1]. In recent years, lifelogging technology has focused on providing information about healthcare and wellness by recording and processing personal user information, behavior, and location with the aid of smart devices [2]. Moreover, applications have also become more popular for providing services relating to specific situations using everyday records [3]. For example, people perform many activities that involve recording daily events, such as taking photos with a camera, writing in a diary, and using social network services (SNS) [4, 5, 6]. Although merely recording events can be useful, it is important to record daily behavior and life, as well as to perform a meaningful analysis of individual patterns and trends. However, most lifelogging technologies focus on recording the user's experiential activities, such as the user's body information, as a physiological signal to identify the emotional state of a moment or to archive photos according to the corresponding locations. That is, no application presently exists that exploits intelligent technology by utilizing the collected information. Photographs are widely used to capture memorable moments in everyday life. In addition, individuals are exposed to the surrounding environment, and human emotions are greatly influenced by surrounding visual information [7]. Visual information such as color and context is known to have a significant influence on human emotions in the fields of marketing and psychology [8; 9; 10]. Several investigations have determined that environmental factors such as color have a notable influence on an individual's purchasing decisions [8; 9]. In addition, research has been conducted on color and the effect of the spatial frequency of the surrounding space on the emotions of an individual within a psychological framework in a business environment [10; 11]. Several studies have also analyzed how sound elements affect humans, specifically in the case of environmental effects or music using waves and voice sounds [12; 13]. In previous works presented in Section 2, most of the research was performed on recognizing simple objects or phenomena in captured scene images. In addition, many previous studies have researched inferring the emotion by analyzing human bio-parameters extracted from bio-signal or images of face and body. However, there is no research on inferring the emotion parameters by analyzing scene video including sounds. Therefore, we propose a method for the detection of features by using the videos, as well as inferring the emotion parameters based on machine learning. This environmental information-based emotional parameter inference method is expected to be combined with the existing biometric information-based personal emotional parameter inference method to implement more accurate emotion recognition technology. In other words, the research motivation of the proposed method is that, in the field of emotional engineering, spatiotemporal environmental factors can influence individual emotions beyond the existing method that relied only on individual biological factors to objectively recognize human emotions.