Аңдатпа
Recent advances in wearable physiological sensing and Internet of Things (IoT) technologies enable new approaches to respiratory training; however, most existing devices rely on fixed mechanical resistance.This study presents a sensor-driven respiratory training system integrating real-time acquisition of physiological and environmental data with controlled airflow regulation.The system comprises a multisensor respiratory mask, a servo-actuated airflow control chamber, and a software layer for real-time data processing, visualization, and supervisory control.Multisensor data are transmitted via a bidirectional Bluetooth link to support system-level monitoring and respiratory load modulation.Sensor modules were calibrated and evaluated against reference instruments under controlled conditions.System validation included assessment of sensor performance, wireless communication stability, and actuator response.A pilot feasibility study involving 20 amateur athletes confirmed robust operation and reproducibility of sensor-guided respiratory load modulation under real training conditions, demonstrating engineering-level feasibility.