Human Activity Recognition and Fall Detection
This project focuses on detecting human activities and identifying falls in real time using machine learning algorithms combined with sensor data. It is particularly valuable for elderly care and health monitoring, as it enables prompt alerts and timely interventions whenever a fall occurs. By automating recognition, the system helps reduce risks, improve safety, and support independent living for vulnerable populations.
“The project can be adapted into healthcare applications, smart homes, or IoT ecosystems for proactive fall prevention and health tracking. Its modular design also allows for future expansion into broader health-monitoring use cases, such as gait analysis, rehabilitation tracking, and fitness monitoring.”
The system relies on data collected from wearable sensors such as accelerometers and gyroscopes. These sensors capture motion patterns that correspond to physical activities including walking, sitting, standing, running, and falling. Various machine learning and deep learning models were developed and tested, with the most effective model achieving high accuracy in real-world scenarios. The solution is lightweight and can be embedded into smartwatches, mobile devices, or IoT-based health monitoring platforms. Continuous monitoring ensures that caregivers and healthcare providers receive instant alerts, while also allowing long-term activity tracking to assess mobility trends and health risks. This integration of AI and wearable technology creates a proactive safety net for elderly individuals and those at higher risk of falls.
