Abstract
The Internet of Things (IoT) idea has arisen as interconnected components of the healthcare tracking facilities of smart linked healthcare networks. Hard sensor-based data aggregation with the help of devices in the form of wearables or intrusive samples attached with the acquisition of soft sensors like crowd sensor results in the aggregated sensor data being concealed in patterns. This problem is tackled through several secret stages of interpretation of deep learning techniques. In this research work, we proposed hybrid deep learning (HDL) techniques to develop estimation and enhance quality of smart health services on health monitoring data. We also showed a detailed comparison of methods on the basis of health surveillance types. Hence, our proposed models work for risk detection in health information which will help us to increase the efficiency of existing healthcare industry.
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Rajawat, A.S., Barhanpurkar, K., Shaw, R.N., Ghosh, A. (2021). Risk Detection in Wireless Body Sensor Networks for Health Monitoring Using Hybrid Deep Learning. In: Mekhilef, S., Favorskaya, M., Pandey, R.K., Shaw, R.N. (eds) Innovations in Electrical and Electronic Engineering. Lecture Notes in Electrical Engineering, vol 756. Springer, Singapore. https://doi.org/10.1007/978-981-16-0749-3_54
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