heart rate
A dataset comprising a total of 21 individuals 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.
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Seventeen male participants (age: 22.8 ± 3.0 years old, height: 1.76 ± 6.2 m, weight: 67.7 ± 5.9 kg, resting heart rate: 66.5 ± 7.0 bpm) without cardiovascular and chronic respiratory problems were recruited. None of the gathered participants had a history of neuromuscular disorders within the past six months. Each participant performed three 30-minute treadmill or terrain running experiences every week in order to maintain his aerobic capability. Participants provided their informed consent after receiving an overview of procedures and potential risks.
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Underwater sampling of heart rate for sports training has growing attention recently because of the availability of new sensors able to gather data while the user is swimming. Namely, optical sensor for the wrist and strap sensor for the chest. Underwater data transmission is not an option, forcing the analysis to be done off-line. Thus, movement and distance from heart could infuence the gap between data from sensors.
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<p> The dataset is digital health data. It contains heart rate data extracted from Fitbit version 2 smartwatch worn by a healthy male Asian person of 48 years old. Data is of one-month duration. We have uploaded a zip file that contains data from different days. Data for each day has a separate file. The file name contains the date. Each file is in csv format. Each file has two columns – timestamp and heart rate. It is a continuous time-series heart rate data. Heart rate was recorded seamlessly at 5 sec interval. However, there may be missing datum.
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The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information.
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Driving practices while HR physiology and pre- and post-EDA were acquired. Stress levels are also rated on a 1-5 scale. The gamer's steering wheel angle, pedals, and steering wheel buttons associated with the driving activity are tracked every 10 msec. The normalized data were stored in Figure 1 in the .xlsx file. Using the Balanced Latin Square method, participants develop each level to avoid level learning when designing experiments with multiple conditions.
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The popularity of wearable physiological recording devices has opened up new possibilities for the assessment of personality traits in everyday life. Compared with traditional questionnaires or laboratory assessments, wearable device-based measurements can collect rich data about individual physiological activities in real-life situations without interfering with normal life, enabling a more comprehensive description of individual differences. The present study aimed to explore the assessment of individuals’ Big-Five personality traits by physiological signals in daily life situations.
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The popularity of wearable physiological recording devices has opened up new possibilities for the assessment of personality traits in everyday life. Compared with traditional questionnaires or laboratory assessments, wearable device-based measurements can collect rich data about individual physiological activities in real-life situations without interfering with normal life, enabling a more comprehensive description of individual differences. The present study aimed to explore the assessment of individuals’ Big-Five personality traits by physiological signals in daily life situations.
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A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.
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For a detailed describtion of this dataset see accompanying publication "Stand-alone Heartbeat Detection in Multidimensional Mechanocardiograms" by Kaisti M., et al. IEEE Sensors 2018, 10.1109/JSEN.2018.2874706. This datasets consists of 29 mechanocardiogram recordings with ECG reference from healthy subjects in supine position. All data were recorded with sensors attached to the sternum using double-sided tape. Mechanocardigrams incude 3-axis accelorometer signals (seismocardiograms) and 3-axis gyroscope signals (gyrocardiograms).
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