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.
Highlights
The Internet of Things (IoT) is a rapidly evolving technology
The current study examined two new Adobos models (MLP and DT) as well as the traditional Maximum Amplitude Algorithm (MAA) method based on predefined characteristic ratios
Wearable devices benefit from PPG signal monitoring with a single sensor and probe
Summary
The Internet of Things (IoT) is a rapidly evolving technology. The Internet of Things (IoT) is about connecting computing devices, mechanical and digital machines, objects, animals, and people with sensors and actuators to collect data and improve wellness, productivity, and efficiency [1]. Using IoT-based remote patient health monitoring is one of the most promising technological interventions emerging to bridge the global health equity gap These IoT technologies are known as the Internet of Medical Things (IoMT). We can get information about our lifestyle, physical and mental performance, living environments, etc., by connecting our bodies to the Internet This allows healthcare providers to monitor human subjects’ health remotely and continuously [2]. It is possible to obtain precise information about body posture and force exertions using direct methods that make use of instruments like inertial measurement units (IMUs) and electromyography (EMG) sensors, among other things. The use of accelerometers to detect the movement of subjects was common [5] These methods, are limited to posture recognition and do not capture information on force exertion, a significant risk factor for WMSDs60. These methods required the use of additional instruments or electrodes on the head, truck, or upper limbs, which was uncomfortable and inconvenient for the user [6]
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