Currently Dogra, Raman Balasubramanian in [3] introduces

Currently
the number of mobile healthcare applications is mounting rapidly and the users
are able to take care of themselves but still there is lack of research about
how the consumer engages with electronic self-observation. Anderson K, Burford
O and Emmerton L in 1 present a valuable investigation of how health clients use
applications for health examining, the benefits the consumers perceive from
usability of health applications and how the applications for health-monitoring
and caring can be improved.

S.M. Riazul Islam, Daehan Kwak, MD. Humaun
Kabir, Mahmud Hossain and Kyung-Sup Kwak conducted a survey about the internet
of things (IOT) healthcare devices in 2. Their proposed paper studies
progresses in IoT-based human services advances and audits the cutting edge
organize structures/stages, applications, and mechanical patterns in IoT-based
social insurance arrangements. What’s more, their paper work investigates
particular IoT security and protection highlights, including security
prerequisites, risk models, and assault scientific categorizations from the
human services point of view. Further, their paper proposes a shrewd community
oriented security model to limit security hazard; examines how unique
advancements, for example, enormous information, surrounding insight, and
wearables can be utilized in a human services setting; addresses different IoT
and e-Health strategies and controls over the world to decide how they can
encourage economies and social orders as far as manageable improvement; and
gives a few roads to future research on IoT-construct medicinal services based
with respect to an arrangement of open issues and difficulties.

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Pradeep Kumar,  Rajkumar
Saini, Pawan Kumar Sahu,  Partha
Pratim Roy, Debi Prosad Dogra,
Raman
Balasubramanian in 3 introduces an assistive structure to function
smart phone through the usage of EEG signals. In their paper, they proposed an
assistive structure “Neuro-telephone” to function smart phones
utilizing Electroencephalographic (EEG) motions by individual with disability.
Their structure can perform fundamental operations of cell phone according to the
mind wave directions. Their investigation of the signs has been performed
utilizing Discrete Fourier Transform (DFT) and the characterization has been
performed utilizing Hidden Markov Model (HMM) classifier. EEG signs of 9 cerebral
instructions from 8 members have been recorded, according to them, utilizing an
Android worked Smartphone. They mention that a precision of 68.69% has been
recorded utilizing HMM based arrangement. The outcomes demonstrate the
viability of their proposed system that can be utilized as a part of future
versatile BCI applications and other human services assistive methods.

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