A Multimodal Dataset of Cardiac, Electrodermal, and Environmental Signals.

In a rapidly evolving technological landscape across various industries, the emergence of real-time, context-aware solutions for health monitoring holds great promise. The dataset presented here encompasses signals from two domains. Ambient environment signals and physiological responses are captured to provide context for well-being assessment. Cardiac activity and electrodermal activity were selected as health indicators, while indoor ambient conditions were characterized by parameters such as temperature, humidity, light, sound, pressure and air quality as determined by Volatile Organic Compounds (VOCs) and Particulate Matter (PM). Data collection involved 14 participants, with each participant contributing approximately 48 minutes of data. This process resulted in a total of over 600 minutes of data, recorded under varied indoor ambient conditions. This dataset was utilized for classifying ambient environment risks concerning long-term cardiac health and for estimating physiological responses exclusively from ambient environment parameters. The compiled dataset provides opportunities for examining the connections between indoor climates and individuals' well-being states in diverse environments, thereby enabling additional investigations and applications in the domain of context-aware technology.