Why the study?
Wearables and Internet-of-Medical-Things devices are key for smart healthcare, motivating the design of a smart garment for continuous physiological data monitoring.
A novel smart garment (MyWear) paired with a deep neural network can classify heartbeat abnormalities with high accuracy, offering a potential tool for continuous remote monitoring.
May enable continuous remote arrhythmia monitoring via wearables; leaves open prospective clinical validation before practice adoption.
Wearables are getting large acceptance in the continuous monitoring of health status and physiological data. Medical devices and their connectivity through Internet along with the electronics health record (EHR) and AI analytics making smart healthcare possible. Internet-of-Medical-Things (IoMT)-end devices like wearables and implantables are key for smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. This paper presents the design and development of a smart garment called MyWear that continuously monitors and collects physiological data. It can analyze muscle activity, stress levels, and heart rate variations and send all the data to the cloud. Through abnormal variations in vitals, it can also predict the risk of heart failure and with the in-built alert system, it can notify the associated medical officials if necessary. We also propose a deep neural network model that classifies heartbeat data into abnormalities with 96.9% accuracy and 97.3% precision.
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Sethuraman et al. (2021) studied this question.
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