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- W4377231605 abstract "The major characteristic of wearable Technology is it is possible to wear on, in, and much closer to the body. Because of said characteristics, it is termed wearable. Their power is driven by microprocessors and improved with the capability to send and receive data through the Internet. In this COVID pandemic era, we realized the necessity for handy wearable devices for the regular monitoring of patients continuously. Fitness activity tracker was the first wearable that attracted the world to divert toward these technologies. The development of devices like wearable ECG and BP monitoring devices and biosensors followed the fitness activity tracker. This technology has transformed into a full-fledged wearable IoT in a very short span. It is very important to collect and analyze the data generated by the devices in such a way that the diagnosis becomes very easy. But, due to the enormous size of the data generation, it becomes difficult to analyze the data effectively. The inventions in machine learning can help us to cope up with the above problem. We can apply an effective machine learning algorithm to the collected data which can perform preprocessing, feature selection, training, and testing and produce an effective prediction of the condition of the patients. This chapter mainly discusses the design, implementation, and effectiveness of the application of ML on data generated by said devices." @default.
- W4377231605 created "2023-05-23" @default.
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- W4377231605 date "2023-01-01" @default.
- W4377231605 modified "2023-10-02" @default.
- W4377231605 title "Machine Learning in Wearable Healthcare Devices" @default.
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- W4377231605 doi "https://doi.org/10.1007/978-981-99-0393-1_13" @default.
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