Camera-Based Analysis of Human Pose for Fall Detection
Chris Cheng Zhang, Chenran Wang, Xinrui Dai, Sicheng Liu · 2023
Falls are a significant public health concern, especially among seniors and older adults, leading to severe injuries, decreased independence, and increased healthcare costs. Automated fall detection systems have emerged as a potential solution to address this issue by detecting falls in real-time and alerting caregivers or emergency services. While traditional fall detection methods rely on wearable devices, challenges arise in accurately detecting falls in complex environments. This report proposes a camera-based analysis of human pose using the Intel RealSense Depth Camera D435 to enhance the accuracy and reliability of fall detection systems. The methodology for this study involves the integration of OpenCV, MediaPipe, and Numpy python libraries to analyze human poses in three-dimensional space. This depth-based analysis includes calculating the depth of pose landmarks, estimating the body's center of mass depth, and tracking the velocity of movement. The findings of this study contribute to the field of fall detection, offering insights into the advantages of a camera-based analysis of human pose and improving the safety of individuals at risk of falls. Future work should include the integration of machine learning techniques and multi-modal approaches to enhance system performance and expand applications beyond fall detection. The technology showcased in this study has implications for healthcare, fitness, rehabilitation monitoring, and elderly care, providing valuable insights to support various domains and improve quality of life.