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Critical Guidelines for Assessing Ventilation:
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Respiratory Assessment: Purpose and Indications01:19

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Respiratory assessment is a cornerstone of nursing assessments, crucial for the early detection of patient deterioration. This evaluation transcends routine procedures, representing a critical skill nurses must master to ensure optimal patient care.
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Oxygen Delivering System I: Nasal Cannula and Face Mask01:26

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The human body requires oxygen to function, and when the natural process of respiration is hindered, external devices, including the following, are needed to help deliver this vital gas.
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Physiological Control of Respiration01:23

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Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
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AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems.

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Summary

This study integrates low-cost air quality (AQ) sensors with 5G and AI to predict 24-hour air quality, enabling early public health warnings. The Multi-Long Short-Term Memory (LSTM) network achieved the best prediction accuracy.

Keywords:
IoTLSTMWSNair pollutionartificial intelligenceforecastinglow-cost sensorsneural networks

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Area of Science:

  • Environmental Science
  • Computer Science
  • Sensor Technology

Background:

  • Low-cost air quality (AQ) sensors offer flexibility and energy efficiency for Wireless Sensor Networks (WSN).
  • Traditional AQ monitoring faces challenges in data complexity and error-proneness, despite the potential of low-cost sensors for enhanced spatial and temporal sampling.
  • Urban areas increasingly require advanced AQ surveillance due to rising respiratory and allergic issues.

Purpose of the Study:

  • To integrate low-cost AQ sensors with 5G communications and Artificial Intelligence (AI) for real-time monitoring.
  • To develop a predictive model for 24-hour ahead AQ readings to provide early public health warnings.
  • To evaluate the performance of different neural network architectures for AQ prediction.

Main Methods:

  • Utilized a network of low-cost AQ sensors and 5G communication infrastructure.
  • Applied Artificial Intelligence (AI) techniques, specifically Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN).
  • Evaluated four neural network architectures: Multi-Linear, Multi-Dense, Multi-Convolutional, and Multi-Long Short-Term Memory (LSTM) networks, trained on a comprehensive dataset.

Main Results:

  • Achieved an average estimation error of approximately 7.2% for most predicted AQ parameters.
  • The Multi-LSTM network demonstrated the best performance for 24-hour ahead AQ predictions.
  • Specific pollutants showed lower errors: CO2 (0.1%), PM10 (2.4%), PM2.5 (2.4%), PM1.0 (2.4%), and NO2 (6.7%).

Conclusions:

  • The integration of low-cost AQ sensors, 5G, and AI (particularly Multi-LSTM) is effective for accurate short-term air quality prediction.
  • This approach can significantly enhance AQ surveillance, especially in urban environments, by providing timely early warnings.
  • The study validates the potential of advanced AI models to improve the utility of low-cost sensor data for public health applications.