Design and Development of Smart, Intelligent & LOT Enabled Remote Health Monitoring System

Drastic developments in the area of Wireless Sensor Networks (WSNs) have paved a path for ap- plications in so called Internet of Things (IoT). Some major applications include remote health monitoring, environment monitoring, smart grids etc. In this work we have identified the primary issues that IoT architectures are facing and developed a system architecture to address the issues identified. IoT architectures primarily face the power constraints due to their battery dependency in remote deployment such as remote health monitoring. Other issue is the hyper connectivity scenario, where the data generated by the sensor networks should have to be limited for efficient management of networks. The proposed architecture consists of an intelligent data transmission mechanism with smart sensing and IEEE 802.15.4 - PHY is considered for communication facility

Performance Analysis of CSMA/CA and PCA for Time Critical Industrial IoT Applications

Recently proposed IEEE 802.15.4-2015 MAC introduced a new Prioritized Contention Access (PCA) for transfer of time-critical packets with lower channel access latency compared to CSMA/CA. In this paper, we first propose a novel Markov chain based analytical model for unslotted CSMA/CA and PCA for industrial applications. The unslotted model is further extended to derive the analytical model for slotted CSMA/CA and PCA. Primary emphasis is laid on understanding the performance of PCA compared to CSMA/CA for different traffic classes in industrial applications. The performance analysis shows the slotted PCA achieves a reduction of 63.3% and 97% in delay and power consumption respectively compared to slotted CSMA/CA, whereas unslotted PCA achieves a delay reduction of 53.3% and reduction of power consumption by 96% compared to unslotted CSMA/CA without any significant loss of reliability. The proposed analytical models for both slotted and unslotted IEEE 802.15.4-2015 MAC offer satisfactory performance with less than 5% error when validated using Monte Carlo simulations. Also, the performance is verified using real-time testbed

Customised Design and Development of Patient Specific 3D Printed Whole Mandible Implant

In this study we assessed the design criteria for the creation of a patient specific, whole mandible implant based on a patient’s medical imaging data and 3D printing. We tailor this procedure to a patient who will undergo a mandibulectomy due to cancer infiltration of the jaw. The patient CT scan data was used to generate a 3D representation of the patient’s skull, before the corrupted mandible was extracted. We examined two approaches based on classical symmetry matching and digital reconstruction of the defect to form the final model for printing. The final designs were then 3D printed and assessed for efficacy against a patient specific representative model of the skull and maxilla, where the final optimised design was found to provide an excellent fit. Ultimately, this technique provides a framework for the design and optimisation of a patient specific whole mandible implant.Mechanical Engineerin

An on-chip robust Real-time Automated Non-invasive Cardiac Remote Health Monitoring Methodology

This paper introduces a novel Real-time Automated Non-invasive Cardiac Remote Health Monitoring Methodology for the detection of human condition by analyzing the ECG signal under the real-time environment. We proposed a novel System-on-Chip (SoC) architecture which has four folded modeling. After the data sensing, firstly, the classification module uses Hurst exponent as a metric in classifying the condition of the ECG signal. Secondly, if there is an abnormal detection by classifier, the Feature Extraction (FE) extracts QRS complex, P wave and T wave from ECG frames. As there is demand of ECG frames, a robust Boundary Detection (BD) mechanism is introduced to identify such frames. The fragmented-QRS (f-QRS) feature is also introduced as our third contribution to detect the fragmentation in the QRS complex and identify its morphology. The fourth step is compressing the ECG data using Hybrid compression technique for low area implementation and communicating this data to the nearest health care center by incorporating the concept of an Adaptive Rule Engine (ARE) based classifier. The proposed SoC architecture is prototyped on Xilinx Virtex 7 FPGA and it is tested on 100 patient's data from PTBDB (MIT-BIH), CSE and in house IITH DB, the percentage of accuracy obtained is 91% and it is under process for Tape-out