Nurse Stress Prediction Wearable Sensors

Dataset for Continuous Stress Monitoring of Hospital Nurses

The growing accessibility of wearable tech has opened doors to continuously monitor various physiological factors. Detecting stress early has become pivotal, aiding individuals in proactively managing their health against the detrimental effects of prolonged stress exposure. This paper presents an exclusive stress detection dataset cultivated within the natural environment of a hospital. Compiled during the COVID-19 outbreak, this dataset encompasses the biometric data of nurses. Analyzing stress in a workplace setting is intricate due to the multifaceted social, cultural, and psychological elements inherent in dealing with stressful circumstances. Hence, our dataset not only encompasses physiological data but also contextual information surrounding stress events. Key physiological metrics such as electrodermal activity, heart rate, and skin temperature of the nurse subjects were continuously monitored. Additionally, a periodic survey administered via smartphones captured contributing factors linked to detected stress events. The database housing these signals, stress occurrences, and survey responses is publicly accessible on Dryad.

Project Overview
This project delves into leveraging wearable device-derived physiological signals to gauge stress levels among nurses operating within a hospital environment. The dataset comprises details acquired from nurses wearing watches that tracked their heart rate, skin temperature, and electrodermal activity (EDA) while simultaneously reporting their stress levels.

The primary goal revolves around evaluating various machine learning models to forecast stress levels based on recorded physiological signals. Additionally, the project investigates the most pertinent physiological indicators for stress detection and offers insights to enhance the accuracy and dependability of stress detection via wearable tech.

Dataset Description:

Data Collection Context:
Period: Data gathered over one week from 15 female nurses aged 30 to 55 years, during regular shifts at a hospital.
Collection Phases: Two phases - Phase-I (April 15, 2020, to August 6, 2020) and Phase-II (October 8, 2020, to December 11, 2020).
Exclusion Criteria: Pregnancy, heavy smoking, mental disorders, chronic or cardiovascular diseases.

Data Captured:
Physiological Variables Monitored: Electrodermal activity, Heart Rate, and skin temperature of the nurse subjects.
Survey Responses: Periodic smartphone-administered surveys capturing contributing factors to detected stress events.
Measurement Technologies: Utilized Empatica E4 for data collection, specifically focusing on Galvanic Skin Response and Blood Volume Pulse (BVP) readings.

Study Procedure:
Approval: University's Institutional Review Board approved the study protocol (FA19–50 INFOR).
Consent and Enrollment: Nurse subjects were enrolled after expressing interest and obtaining hospital compliance.
Study Design: Conducted in three phases, each including 7 nurses. No incentives were provided, and anonymization of data was ensured.

Data Availability:
Public Release: A database containing signals, stress events, and survey responses is publicly available on Dryad.
Anonymization: Unique identifiers assigned to subjects to maintain anonymity.

Merge CSV File Information:
This dataset comprises approximately 11.5 million entries across nine columns:
X, Y, Z: Orientation data (256 unique entries each).
EDA, HR, TEMP: Physiological measurements (EDA: 274,452 unique, HR: 6,268 unique, TEMP: 599 unique).
id: 18 categorical identifiers.
datetime: Extensive date and time entries (10.6 million unique).
label: Categorical states or classes (three unique entries).
The dataset offers a wide array of continuous physiological measurements alongside orientation data, facilitating stress detection, health monitoring, and related research endeavours.

Requirements
Python 3.7 or higher and Jupyter Notebook are prerequisites. The necessary Python packages are enumerated in the requirements.txt file. To execute the code, installation of the following libraries is mandatory: pandas, numpy, sci-kit-learn, and matplotlib.