Sudden Fall Detection of Human Body Using Transformer Model.

In human activity recognition, accurate and timely fall detection is essential in healthcare, particularly for monitoring the elderly, where quick responses can prevent severe consequences. This study presents a new fall detection model built on a transformer architecture, which focuses on the movement speeds of key body points tracked using the MediaPipe library. By continuously monitoring these key points in video data, the model calculates real-time speed changes that signal potential falls. The transformer's attention mechanism enables it to catch even slight shifts in movement, achieving an accuracy of 97.6% while significantly reducing false alarms compared to traditional methods. This approach has practical applications in settings like elderly care facilities and home monitoring systems, where reliable fall detection can support faster intervention. By homing in on the dynamics of movement, this model improves both accuracy and reliability, making it suitable for various real-world situations. Overall, it offers a promising solution for enhancing safety and care for vulnerable populations in diverse environments.