AIP Advances · 2024 · 18 citations · 45 references
EngineeringBioengineeringBiomedical EngineeringBiomedical Signal ProcessingBiosensorsSensorsCardiovascular MonitoringPoint-of-care Diagnostic DevicesVital Signs AssessmentVital Signs RecordingWearable BiosensorsWearable Health TechnologiesControlled Experiment DesignHeart RateHeart Rate MonitoringPhysiological MonitoringPhysiological Signal MonitoringReal-time MonitoringStress MonitoringWearable Physiological Sensors
Stress can increase the heart rate, causing dangerous conditions that cause significant harm and even death. Therefore, managing stress well to control the heart rate is vital. Monitoring stress levels using biomedical devices is essential to prevent further damage to the individual. Regular monitoring facilitates the acquisition of individual stress levels through heart rate information. In this research, a MAX30100 sensor can produce Beats Per Minute (BPM) data, which show the stress experienced by a person during four daily activities (sitting, walking, running, and sleeping) and when watching a horror movie. This research underscores the potential of cost-effective wireless solutions to increase portability and facilitate real-time monitoring and remote surveillance. The researchers introduced an innovative methodology by reusing everyday consumer devices, initially intended for routine activities, to measure stress levels in a unique context—precisely in a controlled experiment involving a movie-watching scenario. The ultimate achievement of this research was creating a stationary heart rate monitoring device that is easy to access, easy to use, space-saving, and adept at effectively tracking stress indicators. BPM readings obtained from the MAX30100 sensor have scores similar to those obtained from the heart rate app. The researchers proposed the inclusion of other sensors, such as a photoplethysmograph, an electrocardiogram, and galvanic skin response, to provide a more holistic assessment of stress. Leveraging cloud technology for data storage enables long-term monitoring and in-depth analysis of stress patterns over time.
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