Health Sensor Data
Description
A dataset comprising a total of 21 individuals, who volunteered from Netaji Subhas University of Technology previously known as Netaji Subhas Institute of Technology, has been meticulously compiled, with 9 individuals identified as exhibiting Major Depressive Disorder (MDD) based on the outcomes derived from the PHQ-9 Questionnaire. The remaining 12 individuals in the dataset are classified as non-MDD. The dataset encompasses diverse sensor data, including temperature measurements, SpO2 readings, pulse rates, and accelerometer data. It is important to note that all data points were collected within a controlled environment, ensuring reliability and consistency throughout the dataset. Columns Temperature Reading in degrees Celcius Pulse Rate Reading in BPM SPO2 Reading % X-Axis Accelerometer Reading
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Steps to reproduce
Below are the steps to reproduce the data- 1. Participant Recruitment and Assessment: Initially, recruit a sample of individuals willing to participate in the study. This recruitment should aim for a balanced representation of individuals with and without Major Depressive Disorder (MDD). Subsequently, administer the Patient Health Questionnaire-9 (PHQ-9) to all participants. This standardized questionnaire assesses depressive symptoms, helping classify participants into MDD and non-MDD groups based on established cutoff scores. 2. Controlled Environment Setup: Establish a controlled environment for data collection to ensure uniformity and reliability across measurements. This controlled setting should minimize external factors that could influence the collected data. Factors such as temperature, noise levels, and lighting should be carefully regulated to maintain consistency throughout the data collection process. 3. Sensor Data Collection: Utilize appropriate sensors and equipment to collect various types of data from each participant. This includes measurements such as temperature, pulse rate, SpO2 levels, and accelerometer readings. Ensure that all sensors are calibrated correctly and positioned appropriately to capture accurate and reliable data. 4. Recording and Synchronization: Record all sensor data meticulously, ensuring proper synchronization and labeling for each participant. This step is crucial to maintaining the integrity of the dataset and facilitating subsequent analysis. Accurate recording and labeling of data will enable researchers to link specific measurements to individual participants and their respective characteristics. 5. Data Analysis and Quality Control: Perform comprehensive data analysis to verify the quality and integrity of the collected dataset. This involves examining the data for any.