Wearable Sensor-Based Human Exhalation Rhythm Recognition using Deep Learning Neural network

Respiratory rhythms are critical in a variety of medical emergencies. Respiratory rhythms of clinical relevance may be detected using a non-invasive respiratory analysis device established in this study. Light-weight wireless sensor nodes attached to the chest and belly of 150 healthy participants were used to gather data on controlled breathing. We next created our own datasets by infusing portions of quiet inhalation with annotated samples of different patterns. For each test datasets, with the one deep neural network has been used to locate the position of every occurrence and categorize it as corresponding to the one of the above-mentioned event kinds. For quiet inhalation, we got a mean F1 score of 93%, for central sleep apnea. Supervised learning may be used to interpret the data through sensing devices, such as chest and abdominal movement, to give a nonintrusive system to measure respiratory rate. Recognizing apneas when sleeping at night and measuring respiratory episodes in ventilated patients medical and surgical patients unit might benefit from this technology.