Welcome to the repository for my Master's research project conducted under the guidance of Professor Anuroop Gaddam for the Masters of Applied Artificial Intelligence degree at Deakin University. This research is centered around leveraging state-of-the-art sensor technologies to facilitate early detection and in-depth analysis of gait disorders in the elderly population.
The primary objective is to explore the interplay between gait dynamics and handrail reliance within smart pathways. By employing synthetic data generation techniques and advanced algorithms, the study aims to develop a comprehensive understanding of how these factors contribute to gait disorders in the elderly.
The proposed solution integrates insights from gait analysis, handrail interaction, and user-specific details to create robust algorithms for real-time monitoring. Through the use of cutting-edge sensor technologies, the research strives to contribute to advancements in healthcare interventions for elderly individuals facing gait challenges.
- Synthetic Data Generation: Employing synthetic data generation methods to simulate diverse scenarios for algorithm testing.
- Algorithm Development: Developing advanced algorithms to analyze gait dynamics and handrail reliance in real-time.
- Smart Pathways: Investigating the role of smart pathways in early detection and monitoring of gait disorders.
- Machine Learning: Utilizing machine learning techniques for refined analysis and predictive modeling.
- Refine methodologies for early detection of gait disorders.
- Enhance understanding of the interplay between gait dynamics and handrail reliance.
- Contribute to advancements in healthcare interventions for the elderly.
Gait Disorders, Elderly Health, Sensor Technologies, Smart Pathways, Synthetic Data Generation, Algorithm Development, Handrail Reliance, Machine Learning, Early Detection, Healthcare Interventions.
Feel free to explore the documents and resources within this repository to gain insights into the research progress and outcomes. Your feedback and collaboration are highly appreciated.