Efficient Remote Health Monitoring Using Deep Learning and Parallel Systems

This study presents a novel approach for non-contact extraction of physiological parameters, such as heart rate and respiratory rate, from facial images captured using RGB cameras, leveraging recent advancements in deep learning and signal processing techniques. The proposed system integrates Artifacts intelligent-driven algorithms for accurately estimating vital signs, addressing key challenges such as variations in lighting conditions, facial orientation, and noise. The system is implemented on both a naive homogeneous architecture and an optimized heterogeneous CPU-GPU system to enhance real-time performance and computational efficiency. A comparative analysis is performed to evaluate processing speed, accuracy, and resource utilization across both architectures. Results demonstrate that the optimized heterogeneous system significantly outperforms the homogeneous counterpart, achieving faster processing times while maintaining high accuracy levels. This performance boost is achieved through parallel computing frameworks such as OpenMP and OpenCL, which allow for efficient resource allocation and scalability. The research highlights the potential of heterogeneous architectures for real-time healthcare applications, including remote patient monitoring and telemedicine, providing a robust solution for future developments in non-invasive health technology.

@article{Khadiri2024,
  title     = {Efficient Remote Health Monitoring Using Deep Learning and Parallel Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Zakaria El Khadiri and Rachid Latif and Amine Saddik},
  doi       = {10.14569/IJACSA.2024.0151020},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151020}
}