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Associate Professor, Department of Mechatronics Engineering, Sona College of Technology , Salem, Tamil Nadu , India
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Professor, Department of Medical Electronics, Velalar College of Engineering and Technology , Erode, Tamil Nadu , India
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Associate Professor, Department of Electronics and Communication Engineering, Karpagam Institute of Technology , Coimbatore, Tamil Nadu , India
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Professor, Department of AI and ML, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), , Chennai, Tamil Nadu , India
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Software Engineer, embedUR systems (India) Private Limited , Chennai, Tamil Nadu , India
Software Engineer, Capgemini , Bengaluru, Karnataka , India
Purpose- The main objective of the proposed paper is to create and implement a real-time wearable health monitoring system based on IoT, i.e., Oxy Sense-Wear, that will enable the constant control of the main physiological parameters, such as ECG, EMG, SpO2, body temperature, and physical activity. The system is aimed at long-term surveillance of the elderly, bedridden, and long-term chronic disease patients, and this allows the patient to identify abnormal health conditions in time and provide proper medical care. Design/methodology/approach-The given device is a soft wearable chest strap with built-in biomedical sensors and powered by an ESP32 microcontroller. Live information is collected, analyzed, and sent through Wi-Fi to a cloud-based server and Android smartphone application. Physiological alerts will activate the buzzer and instant mobile notification when the physiological thresholds are surpassed. The software was used to design and simulate the hardware that was being developed with Proteus and create firmware in the Arduino IDE and the mobile application on Android Studio. Findings- There is a reliable real-time performance as experimental assessment shows heart rate changes with a deviation of +/-2 BPM, SpO 2 values were always in the range of 96-98, and body temperature was monitored accurately between 36.0 o C and 38.8 o C. Fall events were identified with great success at acceleration levels more than 2.5 g, and low false positives. The system recorded a mean alert latency of less than 500 ms and could operate continuously (8 to 12 hours, depending on charge) and thus proved to be viable in the case of personal and clinical remote healthcare monitoring. Originality/value-The proposed Oxy Sense-Wear platform is the first to offer a single multi-parameter sensing, real-time alerting, cloud synchronization, mobile connectivity, and OTA-enhanced platform in a small and wearable size. The work done in the future will be on the implementation of more sophisticated machine-learning algorithms for predictive health analytics, improving the security of the collected data by using encrypted authentication to provide more connection options with the use of the BLE and 5G technology to support the large-scale implementation and integration with the hospital information system. In an effort to be more concise and clearer, this manuscript lays emphasis on system-level insights, comparative appraisal, and quantitative performance assessment rather than a description of the components at a much more detailed level.
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