The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal cameras. The recordings were used to develop and compare several respiratory signal extraction algorithms. An algorithm for the robust extraction of multiple respiratory features was developed and evaluated, capable of differentiating a wide range of respiratory patterns. These features were used to train a one vs. one multiclass support vector machine, which can distinguish between breathing patterns with an accuracy of 95.79 %. The recorded dataset was published to enable further improvement of contactless breathing pattern classification, especially for complex breathing patterns.
Sensors, Free Full-Text, mini box ribeiro
PAPER SENSOR|PRODUCTS|KODENSHI CORP.
160+ ESP32 Projects, Tutorials and Guides with Arduino IDE
Sensors, Free Full-Text, rule 63 urban dictionary
Sensor Technologies: Healthcare, Wellness and Environmental Applications
Sensors An Open Access Journal from MDPI
Sensors, Free Full-Text, rule 63 urban dictionary
Sensors, Free Full-Text
Sensors Free Full-Text Chest X-ray Classification For The, 52% OFF
Sensors Free Full-Text The Design of Large Scale IP Address and Port Scanning Tool - Mag Moreira - Estrategista Legal de Negócios Digitais
STMicroelectronics: Our technology starts with you