# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 3 Dataset Description  
Several datasets exist in the literature that acquire multimodal physiological data in the field of emotion recognition, among them DEAP [53], MAHNOB-HCI [54], EMDB [55], AMIGOS [56], ASCERTAIN [57], CASE [58], CLAS [35], and CLAWDAS [32,33]. Three main criteria have been applied for the selection of the two datasets, CLAS and CLAWDAS, considered here: the presence of cognitive load tasks, the use of wearable devices for physiological signals' acquisition, and the presence of a baseline in the resting state condition. Both selected datasets fit these criteria, thus resulting in being suitable for the analysis proposed. In particular, CLAWDAS was selected as the proprietary dataset, while CLAS was chosen for the high number of participants involved, as well as for the use of the same wearable devices adopted in CLAWDAS. Below, the two datasets are described in detail. In the CLAS dataset, the physiological signals of 60 healthy volunteers (mostly students between 20 and 27 years old, 17 women) were acquired while they were performing interactive or perceptive tasks. In particular, the interactive tasks were introduced to evaluate the level of concentration and the cognitive capacity of different individuals by solving Math Problems, Logic Problems, and Stroop Tests. In the perceptive tasks, different emotions were elicited in the participants by images and video selected from the DEAP dataset [53]. During the whole experiment, three types of physiological signals were simultaneously recorded by means of Shimmer sensors [34] Electrocardiography (ECG), Plethysmography (PPG), and Electrodermal Activity (EDA). The signals were acquired with a sampling rate of 256 Hz and a resolution of 16 bits per sample. In addiction, for each subject, 3D accelerometer data and metadata were also collected. For the purpose of this work, only PPG signals collected during the interactive tasks were considered. For each participant, this phase of the experiment was characterized by the following steps:  
* 1 min of Baseline (BL) in the resting state condition;
* 3 min of Math Problems in which the participant solves different simple Mathematical Problems (MPs) in a limited interval of time;
* 30 s of the Neutral State (NS), in which neutral audio-visual stimuli are displayed;
* 3 min of Stroop Tests (STs), where the user is expected to correctly match the color of the text with the meaning of the word, having a strict time constraint for each assignment;
* 30 s of the Neutral State (NS), in which neutral audio-visual stimuli are displayed;
* 5 min of Logic Problems (LPs) consisting of several simple logical problems often used during IQ tests;
* 30 s of the Neutral State (NS), in which neutral audio-visual stimuli are displayed. Moreover, the 3 Neutral State repetitions of 30 s for each subject in the picture test of the perceptive session were also considered. For further details of this dataset, please refer to [35]. CLAWDAS is a dataset collected in a controlled laboratory environment at the Research Center for Advanced Science and Technology (RCAST) at The University of Tokyo. The experiments performed at RCAST were focused on finding differences in physiological responses related to different ages of the subjects, involved in several tasks, from cognitive to listening and walking ones. The experiments involved two different groups of subjects: a population of 16 Japanese young adults with average age = 24.7 years old (4 women) and a population of 20 Japanese elderly people with average age = 65.15 years old (10 women). During the whole experiment, the heartbeat of each participant was collected though Photoplethysmography (PPG) using the Shimmer3 GSR+ Unit [34] with a sampling frequency of 128 Hz. In addition to PPG, the Electrodermal Activity (EDA) of each subject was acquired using the same sensor.