The dataset contains multiple columns capturing both physiological and demographic data. Physiological data, collected using the Empatica EmbracePlus smartwatch, includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. The processed data is aggregated on a per-minute basis. Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks. The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.
Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis / Morillo, Albarran. - (2024). [10.5281/zenodo.13906740]
Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis
Albarran Morillo
2024
Abstract
The dataset contains multiple columns capturing both physiological and demographic data. Physiological data, collected using the Empatica EmbracePlus smartwatch, includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. The processed data is aggregated on a per-minute basis. Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks. The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2999244