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<p>This work addresses the growing demand for affordable, wearable, and continuous health monitoring, particularly for patients, seniors, and athletes. It presents a wearable system for monitoring five vital signs: heart rate (HR), body temperature (T), blood oxygen saturation (SpO<sub>2</sub>), blood pressure (BP), and respiratory rate (RR). All these parameters are measured using a single MAX30102 module without the need for a cuff or any additional sensors. Using advanced MATLAB signal processing, we have developed a random forest regression method, which, after simplification into a quadratic equation, estimates BP and RR with good accuracy. All recorded data are time-stamped and geotagged on a memory card, enabling patient history to support improved diagnostics. This compact, cost-effective device tracks vital signs and features intelligent alerts-audible alarms notify users of abnormalities. In critical situations, real-time vital signs and GPS coordinates are transmitted to the next of kin for emergency response. This combination of practical design and AI-powered analysis provides an effective solution for personal and clinical applications. Evaluation results demonstrate high accuracy: SpO<sub>2</sub> at 98.74 ± 0.99, T at 98.56 ± 0.48, HR at 95.47 ± 4.31, RR at 95.01 ± 0.96, and clinically acceptable BP estimates (systolic 94.20 ± 8.24, diastolic 92.68 ± 7.37).</p>

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Design and prototyping of an AI-powered wearable device for continuous vital signs monitoring with intelligent alerting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • HuriyeSadat Sadeghi,
  • Mohsen Ahmadi,
  • Daniel Rajabi

摘要

This work addresses the growing demand for affordable, wearable, and continuous health monitoring, particularly for patients, seniors, and athletes. It presents a wearable system for monitoring five vital signs: heart rate (HR), body temperature (T), blood oxygen saturation (SpO2), blood pressure (BP), and respiratory rate (RR). All these parameters are measured using a single MAX30102 module without the need for a cuff or any additional sensors. Using advanced MATLAB signal processing, we have developed a random forest regression method, which, after simplification into a quadratic equation, estimates BP and RR with good accuracy. All recorded data are time-stamped and geotagged on a memory card, enabling patient history to support improved diagnostics. This compact, cost-effective device tracks vital signs and features intelligent alerts-audible alarms notify users of abnormalities. In critical situations, real-time vital signs and GPS coordinates are transmitted to the next of kin for emergency response. This combination of practical design and AI-powered analysis provides an effective solution for personal and clinical applications. Evaluation results demonstrate high accuracy: SpO2 at 98.74 ± 0.99, T at 98.56 ± 0.48, HR at 95.47 ± 4.31, RR at 95.01 ± 0.96, and clinically acceptable BP estimates (systolic 94.20 ± 8.24, diastolic 92.68 ± 7.37).