Abstract
With the rise and advancement of technology, early detection and involvement in health-associated monitoring through home control are growing with population aging. The expansion of healthy life expectations is progressively significant due to the speedy aging of the world population. The patient requires early and home-based treatment to detect and prevent disease on time and with less effort. Home-based health monitoring has been considered the need of a smart home. The services of health monitoring can facilitate the patient by collecting and analyzing the data of health for tackling diverse complex issues of health at a large scale. Health monitoring is a sustainable progression of clinical trials for ensuring that health is monitored according to the defined protocol and standard operating procedures. Various scenarios can be considered for monitoring health and are performed through experts of the field. Healthcare systems are having large-scale infrastructure of electronic devices, medical information systems, wearable and smart devices, medical records, and handheld devices. The growth in medical infrastructure, combined with the development of computational approaches in healthcare, has empowered practitioners and researchers to devise a novel solution in the innovative spectra. A detailed report of the existing literature in terms of deep learning and transfer learning is the dire need and facilitating of modern healthcare. To overcome these limitations, therefore, the proposed study presents a comprehensive review of the existing approaches, techniques, and methods associated with deep learning and transfer learning for health monitoring. This review will help researchers to formulate new ideas for facilitating healthcare based on the existing evidence.
Highlights
The technology has ever played a significant role in healthcare and is providing facilities of being digitally transmuted
The data and information of patients are warehoused in a consistent mode to certainly store, access, and retrieve the essential associated information
Smart IoT-based applications such as electronic medical report generators, wearable devices, and smart mobile phone healthcare systems have transformed the systems of conventional healthcare to the systems of digital healthcare
Summary
The technology has ever played a significant role in healthcare and is providing facilities of being digitally transmuted. Wearable devices, and ambient surroundings devices are integrated with variation of smart sensors such as magnetometer, heart rate, accelerometers, and pressure and wearable camera for detecting and monitoring activities [6] These sensors are preprocessed, and various feature sets like frequency domain, time domain, and wavelet transform are extracted and transmuted through algorithms of machine learning for monitoring and classification of human activities. The existing research in the field requires a detailed report of the current literature in terms of deep learning and transfer learning to facilitate modern healthcare. To overcome these limitations, the proposed study aims at presenting a wide-ranging review of the existing techniques, approaches, and methods related with deep learning and transfer learning for health monitoring.
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