DOI: 10.5061/dryad.5hqbzkh6f
title: A multi-modal sensor dataset for continuous stress detection of nurses in a hospital

description: Advances in wearable technologies provide the opportunity to monitor many
 physiological variables continuously. Stress detection has gained
 increased attention in recent years, especially because early stress
 detection can help individuals better manage health to minimize the
 negative impacts of long-term stress exposure. This paper provides a
 unique stress detection dataset created in a natural working environment
 in a hospital. This dataset is a collection of biometric data of nurses
 during the COVID-19 outbreak. Studying stress in a work environment is
 complex due to the influence of many social, cultural, and individuals
 experience in dealing with stressful conditions. In order to address these
 concerns, we captured both the physiological data and associated context
 pertaining to the stress events. We monitored specific physiological
 variables, including electrodermal activity, heart rate, skin temperature,
 and accelerometer data of the nurse subjects. A periodic
 smartphone-administered survey also captured the contributing factors for
 the detected stress events. A database containing the signals, stress
 events, and survey responses is available upon request.
description: The data was gathered for approximately one week from 15 female
 nurses working regular shifts at a hospital. 1,250 hours worth of data was
 collected in two study sessions in Apr-May and Nov-Dec of 2020. The data
 was collected using Empatica E4 wearable devices. A survey was
 administered every day to identify the type of stress.
description: <strong>Data
 description</strong> The following is a
 description of directories and files in the dataset.
 Stress_dataset.zip: The zip file holds the
 data of 15 participants in different folders. Each folder contains raw
 data signals in CSV format in a sub-folder. A raw data folder consists of
 6 different CSV files, including (1) EDA.csv (electrodermal activity), (2)
 HR.csv (heart rate), (3) TEMP.csv (skin temperature), (4) IBI.csv
 (inter-beat interval), (5) BVP.csv (blood volume pulse), and (6) ACC.csv
 (accelerometer data). Each biometric signal data has
 the following information: Start time
 (epoch): The DateTime float number that contains the time that signal was
 generated using the internal clock of the wristband. The DateTime is
 stored at the first row of every data column.
 Frequency: The second cell of each column shows the data
 collection frequency ACC.csv:
 Column I: x-Axis acceleration
 Column II: y-Axis acceleration Column III:
 z-Axis acceleration BVP.csv:
 Column I: Blood volume pulse is a method of measuring
 the heart rate. EDA.csv:
 Column I: Electrodermal activity of the skin,
 measuring the skin’s electrical conductivity. 
 IBI.csv: Column I: time
 interval Column II: Inter-beat interval or
 beat-to-beat interval, being the time interval between individual beats of
 an individual’s heart. TEMP.csv:
 Column I: Skin temperature in Celsius.
 tags.csv: contains the timestamp of the user tag. A
 tag event occurs when the user clicks the button on the watch to mark an
 event. However, the subjects did not consistently use this feature and the
 field has no information value in our study. In some
 cases, the sensor data csv files are empty. This constitutes a failure of
 the device to capture data. <strong>Survey
 Results File</strong> Each folder name is
 identical to the participant’s ID in both data and survey files. All of
 the signals were synchronized to bring them to a common frequency. The
 accelerometer data is not used in the stress detection model. Some of the
 basic physical activities can be estimated from the accelerometer
 sensor, which could be further used to potentially include the activity
 context in stress detection.
 SurveyResults.xlsx: The Excel file holds all
 participant survey results and their annotated stress level in Excel
 sheets (a sheet for each participant). Sheet names are the participant’s
 IDs. However, the IDs are generated in an ID column for all files for more
 convenience. The following are the excel sheet columns:
 Column A: ID - Anonymized Id of the user.
 Column B: Start time - Event start time.
 Column C: End time - Event start time.
 Column D: Duration - Duration of the event.
 Column E: Date - Date of data collection.
 Column F: Stress level - Reported stress level by the
 nurse. Nurses' responses regarding
 the nature of the stress. Column G: COVID
 Related Column H: Treating a COVID patient
 Column I: Patient in Crisis Column J:
 Patient or patient’s family Column K: Doctors or
 colleagues Column L: Administration, lab, pharmacy,
 radiology, or other ancillary services Column M:
 Increased Workload Column N: Technology related
 stress Column O: Lack of supplies
 Column P: Documentation Column Q: Safety
 (physical or physiological threats) Column R: Lack of
 supplies Column S: Work Environment - Physical or
 others: work processes or procedures Column T:
 Description