# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 1 Introduction  
In recent years, sensor technology has significantly improved and wearable devices have become increasingly popular, allowing easily registering subjects' physiological responses during their daily activities [1, 2]. Physiological signals are successfully used to measure arousal [3, 4, 5]. Arousal is an uncontrolled human reaction, related to attention and cognitive alertness, activated by stimuli that require high psycho-physical engagement, and thus activated in particular during cognitive tasks and stressful conditions. Although it has been proven that, sometimes, stress can have a positive effect on a person by improving his/her alertness state or his/her ability to react [6], it has also been proven that a high and continuous level of stress or cognitive load can affect the physical and mental well-being of the subject. Illness such as depression, anxiety, and sleep disorders are, indeed, often due to excessive stress or workload [7]. In view of its importance, the automatic recognition of stress and excessive cognitive load has recently become an object of study, even in different application areas. For instance, systems able to recognize emotion and, above all, stress experienced by subjects can be used in working or academic environments [8] in order to monitor and identify the emotional state of employees or students. In this regard, it has been proven that a high level of stress or cognitive load due to excessive workload can increase the level of fatigue, decrease the subject's working capability, and consequently, bring physical and mental illness, which can lead to workplace absence [9]. Similarly, automatic stress recognition systems can be used in the context of vehicle driving for the detection of excessive mental fatigue states, which can reduce a person's driving skills [10]. Finally, algorithms of stress detection can also be used in recreational areas for the development of systems able to modify their parametersbased on the user's emotional state. Concrete examples concern music-retrieval systems able to interact with a user to suggest a music playlist using both external inputs and his/her physiological signals [11] or video games in which some internal game parameters, such as the difficulty, are set based on the player's emotions and stress level [12]. Moreover, systems able to recognize the subject's emotional state can also be involved in the medical area. For instance, emotion recognition systems can be used to monitor the health state of convalescent patients [6] or to help elderly subjects during their daily activities [13,14]. In all of these contexts, the development of systems able to recognize, interpret, and simulate human affect can be seen as a necessary step to make technologies user friendly and able to interact actively with people. Several works in the literature have focused their attention on the analysis of physiological signals as an honest indicator of humans' emotion and mood [3,4]. In particular, the heart rate of a person is positively correlated with the perceived level of arousal and stress [15], thus resulting in being promising in the recognition of this specific emotional state [16]. Several stressors have been adopted in the literature to elicit arousal and mental stress [17]. Among them, mental arithmetic calculus or the Stroop test has been applied as a stressor in numerous experiments to evoke a high level of cognitive load in the participants [18]. In many of these studies, the use of physiological signals related to heartbeat has allowed positive performance both in statistical [19,20] and classification analysis [21, 22, 23]. Despite the progress in sensor technology and the relative simplicity of acquiring physiological signals from the human body, there are still some critical issues that must be addressed to fully exploit the potential that the analysis of physiological signals can offer.