Abstract:
This research presents a Pose Estimation system implemented in Python, specifically tailored for the unique needs of elderly individuals. Leveraging computer vision techniques, the system captures and analyzes body poses, offering valuable insights into the physical activities and movements of elderly people. The goal is to employ technology to improve healthcare monitoring, encourage physical activity, and enhance the overall well-being of the elderly population.
The Pose Estimation system utilizes Python’s computer vision libraries, such as OpenCV and PoseNet, to accurately capture and interpret body poses from images or video feeds. Machine learning models, trained on diverse datasets encompassing elderly individuals, enable the system to recognize a range of poses and movements associated with daily activities.
Key features of the system include real-time pose analysis, automated anomaly detection for identifying deviations from normal movements, and personalized feedback to encourage exercises or activities beneficial for health. The system can be integrated with wearable devices or home monitoring systems to provide a holistic view of an individual’s physical well-being.
The open-source nature of the implementation encourages collaboration and customization, allowing developers to adapt the system to different healthcare contexts or rehabilitation programs. The research evaluates the system’s accuracy and usability through testing with elderly participants, assessing its potential for seamless integration into daily life.
By providing a Pose Estimation system for elderly individuals in Python, this project contributes to the advancement of assistive technologies in healthcare. The system stands as a valuable tool for caregivers, healthcare professionals, and families, offering a non-intrusive means of monitoring and promoting the health and independence of the elderly population through technology-driven insights into their physical activities.