Currently, wireless communication network applications are becoming part of our daily life that cannot be ignored. One of the most important is healthcare application. Physiological and health condition of a person can be detected, processed, transported, and stored easily in (wireless) body area network (W)(BAN). WBANs have several applications including in sports, interactive gaming, military, and security. For instance, aged people show larger dependence on the healthcare system because of age related diseases like cardiac ailments, respiratory problems, arthritis, neurological diseases, and dementia. According to World Health Organization (WHO), by 2047 people 60 and above will be 2 billion, up from 841 million in 2008. According to the US Bureau of the Census, in US alone elderly people are expected to 70 million by 2025, the healthcare expenditure is about $5.4 trillion, which will represent 20% of the GDP. Hence, smart and interactive wireless healthcare system can leverage both the health and economic issues of users.
The three-tier communication architecture of WBAN consists of, (i) BAN node: each node is integrated with biosensors (ECG, SpO2, temperature, etc.) to record patient’s dynamic body parameters and movements; (ii) LPU (Local Processing Unit): to gather data from BSNs and provide to the physicians. It also a router between BAN nodes and the central server using Bluetooth and Wi-Fi for short range and mobile networks for long-range transmission. (iii) back-end infrastructure: which consists of (a) CS (Central Sever), which feeds the patient data to the PD (Patient Database) and (b) Physician Workstation.
The challenges in WBAN are, reliable data transmission, node mobility support and fast event detection, timely delivery of data, power management, and security. Medical data networks are increasingly exposed to external attacks. Safety and privacy of medical data must be guaranteed all the way from the sensor nodes to the back-end services. On the other hand, energy efficiency issue exists at different sensor nodes, and communication and data processing subsystems.
This work focuses on developing a new FPGA based body area anomaly detection system using machine learning techniques by training the behavioral change of body area environment (i.e., indoor and outdoor). Because FPGA is an integrated circuit that contains a large resource of logic gates and memory, it is possible to implement parallel digital computation and executions. This leads to low latency and minimum energy consumption. Hence, we propose system on Chip (SoC) (i.e., Microcontroller Unit (MCU), Central Processing Unit (CPU), and FPGA) at both ends of WBAN system. It enables a wide range of healthcare applications such as ubiquitous health monitoring (UHM), computer assisted rehabilitation, emergency medical response system (EMRS), and promoting healthy living styles.
The objective of this work is to design an energy efficient and secure WBAN for pervasive healthcare system, which reduce patients visit to hospitals. Specifically, to transmit and store medical data securely keeping the privacy, authenticity, availability, and integrity of medical data at the three layers of the WBAN architecture, and to model a power efficient WBAN during data processing and transmission.

Project Proposal

1. High-level project introduction and performance expectation

2. Block Diagram

3. Expected sustainability results, projected resource savings