Overview / Usage

This project includes the development of an mHealth (mobile health) application with a smart belt that can monitor and analyze the spinal cord in real time. The goal is to solve problems related to the health of the spine, such as monitoring movement patterns, controlling imbalances, and providing personalized recommendations for exercise and stretching to aid recovery and prevention.

Sensor-intelligent belt that collects information about spinal angle, strain and pressure. This data is collected, analyzed and sent to applications via IoT technology. The application, available on Android and iOS platforms, provides users with access to real-time activity data, personal activity and reports.
The app also acts as a communication system, sharing information with doctors and family members.

The study/research in this project includes the use of Intel oneAPI and SYCL to improve application performance and performance. Comparing computing and hardware acceleration, the project optimizes data processing, analysis and machine learning algorithms for real-time monitoring. Using Intel oneAPI and SYCL can effectively use Intel hardware accelerators such as GPUs and FPGAs to increase computing power.

An mHealth application developed with a smart belt on the production side can be used in a real medical environment. People with spinal problems can be used by doctors and integrated into a larger medical system. The app can facilitate remote monitoring of a patient's spinal health, providing insight and personalized recommendations for treatment and prevention. Lessons learned from this study can guide the development and implementation of similar mHealth solutions for spinal cord care and management in hospitals and clinics.

Methodology / Approach

This approach includes using technology to create integrated solutions when it comes to spinal cord care and management. Below is a description of the methods and procedures used:

Problem Identification: The first step is to identify the problem that requires real-time monitoring and spine analysis to address spinal health issues. This includes understanding patients' problems with spinal cord problems and their needs for effective care and treatment.

Technology Selection: The next step is to select the appropriate technology to build the solution. In this case, Intel oneAPI and SYCL are the technologies of choice, providing a strong foundation for parallel computing and hardware acceleration.This technology provides efficient data processing and analysis necessary for timely monitoring and personalized recommendations.

Kernel Development: Kernel development is about using Intel oneAPI and SYCL. SYCL serves as a programming standard and standard that allows developers to write code in parallel with C. Intel oneAPI DPC Compiler is used to compile and optimize SYCL code for execution on Intel hardware accelerators.

Standards and Technology: This service follows industry standards and software development best practices.

Technologies Used

Technologies, libraries, tools, software, hardware and Intel technologies used in the development of this project include:

Technologies:
Intel oneAPI: Integration Software Programming to Design Different Applications including CPUs, GPUs and FPGA Model.
SYCL (Standard C for Heterogeneous Parallel Programming): A functional model and standard that enables developers to write parallel code for heterogeneous devices using C.

Libraries:
oneAPI DPC++ Library (oneDPL): A library that provides a parallel framework for data manipulation and analysis.
oneAPI Math Core Library (oneMKL): A library for optimizing math to improve performance.
Tools:
Intel DPC++ Compiler: A special compiler designed for the oneAPI function that translates SYCL code into a special tool for execution on different devices.

Software:
Android or iOS Application Development Tools:

Android and iOS platform specific tools and frameworks for developing mobile application interfaces.

Data Analysis and Machine Learning Techniques:

Software techniques such as TensorFlow, PyTorch or scikit-learn can be used for data analysis and machine learning.

Hardware:
Intel CPU: Intel's central processing unit that provides computing resources.
Intel GPU: Graphics processing unit from Intel, known for its parallel processing capabilities and hardware acceleration.
Intel FPGA: Field Programmable Gate Array from Intel that provides programmable hardware for custom computations.

Intel Technologies:
Intel DevCloud for oneAPI: A cloud development environment that provides access to Intel hardware and software tools for testing and optimizing oneAPI/SYCL applications on different shared devices.
These technologies, libraries, tools, software and hardware together support the development of mHealth applications with intelligent, integrated computing, hardware acceleration and optimized algorithms for efficient data, analysis and backbone maintenance.

Documents and Presentations

Repository

https://github.com/Srilekha-09/mhealth