Open	M M Arjun is studying Computer Science and Engineering at the RV College of Engineering

Poster Abstract

Introduction:
The advancement in healthcare needs a revolutionary transformation and artificial intelligence (AI) integration holds the key to this change. By harnessing advanced big data technologies like HPCC Systems, healthcare practitioners can efficiently manage and analyze vast quantities of legal data, extracting valuable insights crucial for healthcare studies. Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. Through big data processing and analysis, the system can uncover meaningful patterns and precedents, enabling more efficient and effective research. The utilization of HPCC Systems facilitates the seamless handling of complex healthcare datasets, enhancing the overall efficiency and accuracy of the entire system. By embracing these innovative technologies and methods, the healthcare sector can adapt to the challenges of the modern world, ultimately leading to fairer and practical outcomes.

Objective:
The goal is to leverage advanced big data technologies, such as HPCC Systems, to effectively manage and analyze vast amounts of healthcare data from the publicly available data from bellabeat case study (FitBit Fitness Tracker data). This involves developing robust data processing and analysis capabilities to extract meaningful insights, identify relevant patterns and precedents, and facilitate comprehensive and accurate healthcare research.

Methodology:

1. Data Processing: Utilize HPCC Systems' Enterprise Control Language (ECL) and query engines for data processing, real-time monitoring, anomaly detection, and predictive modelling.
2. Analytics: Perform trend analysis, correlation, and other analytics tasks on wearable device data to derive valuable insights.
3. Visualization: Create interactive visualizations and reports using HPCC Systems' tools like ECL Watch and Roxie Control for effective data communication.
4. Evaluation and Improvement: Continuously evaluate system performance, seek feedback from healthcare professionals, and iterate on the solution to enhance its effectiveness.
By following this methodology, healthcare organizations can harness the capabilities of HPCC Systems to process and analyze data from wearable devices, leading to improved patient monitoring, personalized care, and better healthcare outcomes

Presentation

In this Video Recording, M M Arjun provides a tour and explanation of his poster content.

HPCC Systems for Healthcare Wearable Devices

Click on the poster for a larger image.

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