SAIS (Stress Analysis using IOT Sensor) Data Set
Predicting the stress level of humans is an enervating task as to determine stress level of humans requires an efficient scheme. For data collection, MySignals toolkit which consists he Arduino Uno board and different sensor ports is used. MySignals is a development platform for medical devices and e-Health applications. It is a multichannel physiological signal recorder which measures more than 15 different biometric parameters such as pulse, breath rate, oxygen in blood, electrocardiogram signals, blood pressure, muscle electromyography signals, glucose levels, galvanic skin response, lung capacity, snore waves, patient position, airflow and body scale parameters (weight, bone mass, body fat, muscle mass, body water, visceral fat, Basal Metabolic Rate and Body Mass Index). Galvanic Skin Response Sensor and Electrocardiogram Sensor were used to acquire data of participants of different age groups. After acquisition and pre-processing, the processed data can be utilized for classification and prediction purposes by applying machine learning and deep learning models to predict the mental state of the users and to understand their physiological characteristics under different circumstances.
Steps to reproduce
Given is the attribute name, attribute type, the measurement unit and a brief description. Name / Data Type / Measurement Unit ----------------------------------------------- gsr_max / continuous / siemen / gsr_min / continuous / siemen / min_ra / continuous / -- / max_ra / continuous / -- / gsr_mean / continuous / siemen / gsr_sd / continuous / siemen / gsr_var/ continuous / -- / ecg_avg / continuous / Volts / ecg_median / continuous / -- / ecg_sd/ continuous / Volts / relax/ integer / -- / stressed / integer / -- / partially stressed / integer / -- / happy / integer / -- / class/integer/ -- / The readme file contains attribute statistics.