A Novel Smart Healthcare Monitoring System Using Machine Learning and the Internet of Things

Abstract

The Internet of Things (IoT) has enabled the invention of smart health monitoring systems. These health monitoring systems can track a person’s mental and physical wellness. Stress, anxiety, and hypertension are key causes of many physical and mental disorders. Age-related problems such as stress, anxiety, and hypertension necessitate specific attention in this setting. Stress, anxiety, and blood pressure monitoring can prevent long-term damage by detecting problems early. This will increase the quality of life and reduce caregiver stress and healthcare costs. Determine fresh technology solutions for real-time stress, anxiety, and blood pressure monitoring using discreet wearable sensors and machine learning approaches. This study created an automated artefact detection method for BP and PPG signals. It was proposed to automatically remove outlier points generated by movement artefacts from the blood pressure signal. Next, eleven features taken from the oscillometric waveform envelope were utilised to analyse the relationship between diastolic blood pressure (SBP) and systolic blood pressure (DBP). This paper validates a proposed computational method for estimating blood pressure. The proposed architecture leverages sophisticated regression to predict systolic and diastolic blood pressure values from PPG signal characteristics.