Smart health monitoring system technologies pertaining to the biomedical domain has evinced intense interest, which in turn has led the emphasis on unconventional applications of engineering and internetwork transmission in the field. Throughout the next series of textual and contextual matter, we will be reviewing and exhibiting, such initiatives and established methods which are being used to achieve health monitoring systems approaching toward making smart remote villages and cities. It also collates for the reader the issues and challenges prevalent in the present technical advancements and for societal validation, a survey analysis has also been briefed in this paper to show the demand and requisites of the real world in health monitoring system. Today there is a need for an integrated solution which can diagnose the early cyanotic congenital diseases in newly born babies to monitoring of various geriatric problems in elderly people. The future avenues of maximum life expectancy and uber-prompt medical treatment depend upon it. This paper proposes an approach required to make smart villages and towns with respect to the healthcare domain. This proposal has been made with due consideration and technical backing of survey research including questionnaire-based evaluation and regressive analysis of issues in present technologies.

Review of Smart Health Monitoring Approaches With Survey Analysis and Proposed Framework

Brain Computer Interface (BCI) has recently gained much popularity due to plethora of its applications. In this paper, we propose a novel system architecture to utilize brain signals for controlling Internet of Things enabled environments. The proposed architecture aids in translating brain signals to commands that interact with or control the environment using IoT actuation networks thus executing user desired actions. It comprises of novel, low complex and low power intelligent signal processing architecture for detection of voluntary eye blinks by isolating involuntary eye blinks and IoT enabled wireless actuation network for controlling the environment using commands generated from EEG signal. For the real time performance analysis of the proposed architecture, we developed a wearable device which acquires dual channel EEG using electrodes at Fp1 and Fp2 locations. From the acquired EEG data, the device detects the voluntary eye blinks of the patient and use this information in controlling the environment such as switching HVAC system, lighting or electric fan etc. Performance analysis shows that the proposed intelligent signal processing architecture detects the voluntary eye blinks with 95.2% accuracy when tested on 10 subjects with a low power consumption of 165 mW.

A novel system architecture for brain controlled IoT enabled environments

IOT devices is used in many fields which make the user’s daily life more comfortable. These smart sensors devices is used to collect heartbeat which is used to assess the health condition of the patient. Communicating the collected information to the doctor, making exact decision on the data collected and notifying the patient is the challenging task in the IOT. This paper will give you a comparative study on health detection and monitoring of the patient.

A Survey on Health Monitoring System by using IOT

This paper proposes two link adaptation strategies for IEEE 802.15.4 wireless personal area networks (WPANs), using a multi-rate signaling set. In the proposed link adaptation strategies, the most adequate modulation and coding scheme (MCS) satisfying the target bit error rate (BER) of the end device is selected based on the signal-to-interference-plus-noise ratio (SINR) of either a beacon or an acknowledgement (ACK) frame. The beacon-based link adaptation scheme has low complexity and overhead, given that it performs link adaptation only once per superframe. In contrast, the ACK-based strategy performs link adaptation at every ACK frame, and therefore provides a faster and more effective link adaptation, but at the expense of a larger overhead. The specific protocol design for the proposed link adaptation strategies is developed by constructing the signal flow based on the service primitives between the protocol stack layers. The network simulator OPNET is used to implement an accurate IEEE 802.15.4 WPAN protocol stack and the simulation environment required for performance evaluation. The simulation results show that the received throughput of IEEE 802.15.4 WPANs can be improved by exploiting the proposed link adaptation strategies instead of auto-rate fallback, the conventional WPAN strategy.

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Link adaptation strategies for IEEE 802.15.4 WPANs: Protocol design and performance evaluation

This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple point-care scenarios for healthcare promotion and cardiovascular disease prevention. The platform has the capability to provide continuous monitoring, extended device integration, strategies based on artificial intelligence for the information analysis and cybersecurity support, delivering a secure end-to-end hardware-software solution. This platform is used to perform the remote patient health monitoring and supervision by doctors, triage procedures in hospitals, or self-care monitoring using personal devices such as tablets and cellphones. The proposed hardware architecture facilitates the integration of biomedical data acquired from different health-point cares, collecting relevant information for the detection of cardiovascular risk through deep-learning algorithms. All these characteristics make our development a strong tool to perform epidemiological profiling and future implementation of strategies for comprehensive cardiovascular risk intervention. The components of the platform are described, and their main functionalities are highlighted.

Platform for Healthcare Promotion and Cardiovascular Disease Prevention

We present a novel smart transmission technique with seamless handoff mechanism to achieve ubiquitous connectivity using multiple on-chip radios targeting remote health monitoring applications. For the first time to the best of our knowledge, a system architecture for low-power ubiquitously connected multiparametric remote health monitoring system is proposed in this paper. The architecture proposed uses a generic adaptive rule engine for classifying the collected multiparametric data from patient and smartly transmit the data when only needed. The on-chip seamless handoff mechanism proposed aids for the ubiquitous connectivity with a very good energy savings by intelligent controlling of the multiple on-chip radios. The performance analysis of the proposed on-chip seamless handoff mechanism along with adaptive rule engine-based smart transmission mechanism achieves on an average of 50.39% of energy saving and 51.01% reduction in duty cycle of transmitter taken over 20 users compared with the continuous transmission. From the hardware complexity analysis made on the proposed seamless handoff controller and adaptive rule engine concludes that they require only 2082 CMOS transistors for real-time implementation.

Malyala Pavana Ravi Sai Kiran

Papers

System architecture for low-power ubiquitously connected remote health monitoring applications with smart transmission mechanism