At Sensor Diagnosis for Smart Healthcare: Probability or Conditional Probability Based Approach vs. k-Nearest Neighbour

At Sensor Diagnosis for Smart Healthcare: Probability or Conditional Probability Based Approach vs. k-Nearest Neighbour

Chetna Laroiya (Jagan Institute of Management Studies (Affiliated to GGSIP University), New Delhi, India) and Vijay Bhushan Aggarwal (University of Illinois, Urbana, USA)
DOI: 10.4018/IJAPUC.2018100101

Abstract

In order to implement IoT-based health-care for improved quality of life, we have to deal with sensor and communication technologies. In this article, the authors propose an approach to analyse real-time data streaming from a patient's surface body sensors, which are to be looked upon in a small sliding window frame. Time series analysis of data from the sensors is effective in reducing the round-trip delay between patient and the medical server. Two algorithms are for the sensor, and odd measures are proposed based on joint probability and joint conditional probability. The proposed algorithms are to be SQL compliant, as traces of at-sensor UDBMS alongside elementary capabilities supports databases with a meagre amount of SQL, which is evident in the literature.
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2. Literature Review

As per the current implementations of ubiquitous healthcare, there is no processing on real time sensor data at data layer. Processing layer is implemented at the top of the data layer. Above processing layer is semantic layer (Zafeiropoulos, Spanos, Arkoulis, & Mitrou, 2009).

MAPS (Mobile agent platform for Sun SPOT) provided the agent-oriented programming abstractions for WSN applications provided is component-based architecture (Aiello, Fortino, & Gravina, 2011). Using MAPS, a WBSN with 2 sensor nodes one at waist and another at thigh of the patient under monitoring along with one coordinator. Coordinator is responsible for real time predefined human activity diagnosis (Aiello, Bellifemine, Fortine, Galzarano, & Gravina, 2011).

Figure 1.

Healthcare system

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Healthcare system in Figure 1 has coordinator node which help to send sensor information to base station for diagnosis. Patient is carrying GPS which provides patient’s location in case of emergency (Aminian & Naji, 2013).

Figure 2.

Gateway based healthcare architecture

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Gateway tree-based healthcare architecture in Figure 2 assist doctors to get real time sensor data from a particular node. Gateway and fixed node are placed on shops or road to route sensor data to the base station. Mobile nodes are patients’ sensor which achieves connection with the node on the gateway tree. In case of emergency, doctors can get patient’s location information using IPv6 (Wang, Hongbin, Cheng, & Xie, 2014). Here sensor node does not initiate data transfer, but doctor can initiate data collection for analysis.

An integrated gateway approach is proposed which receives measurements from various personal health devices (PHDs) and communicate it to remote monitoring server (MS). Immediate transmission for those measures which exceed the threshold and integrated transmission for usual measures (Park & Pak, 2012).

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