-Camelia Bhattacharyya, Amity University Kolkata
Dave Waters once said, “A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning”. This professor or scientist and researcher also said, “Machine learning will automate jobs that most people thought could only be done by people”. The present scenario has established the reality of these two lines beautifully and smartly. Artificial intelligence (AI) has not only taken over agriculture, sports, and vehicles but also other areas like the medical field. Recent studies have shown its application in determining even the respiratory behaviors of the patients.
What if we could continuously monitor the respiratory behaviors? Since the currently used techniques have proved themselves inefficient to accurately distinguish different disorders related to the lungs, AI has somehow managed to show some positivity in this field to make detection easy. The detection is based on different postures. The sensors are attached between the xiphoid process and the costal margin and the position is a centimeter above the umbilicus. These sensors due to the presence of ultrasound emitter, wireless transmitter ultrasound receiver, and data acquisition, can accurately extract different data of the temporal respiratory behavior and store them in a laptop. These sensors are not only accurate and lightweight but also can detect different respiratory disorders like asthma, chronic respiratory obstructive disease (COPD), apnea, etc at the starting stage and regularly for better treatment and cure.
AI has does prove to be an efficient research area in every field, making life easier and safer than ever before.
Also read: A critical role of intraspecific host variation in pathogen community structure
SOURCE: Ang Chen, Jianwei Zhang, Liangkai Zhao, Rachel Diane Rhoades, Dong-Yun Kim, Ning Wu, Jianming Liang, Junseok Chae. Machine-learning enabled wireless wearable sensors to study the individuality of respiratory behaviors. (2020). https://doi.org/10.1016/j.bios.2020.112799
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