Human Fall Motion Prediction – A Review

Abstract

Abstract – In predicting human fall motion, focused on enhancing safety and quality of life for the elderly and individuals at risk of falls. By highlighting the critical role of Human Pose Estimation, advancements in human motion forecasting, and fall prediction. It explores the continuous efforts to improve fall detection systems using innovative technologies, such as wearable sensors and IoT devices to implement deep learning models and analyze human poses and gestures. Various methods show promise in accurately predicting human fall motion by capturing complex patterns and relationships in the data. For instance, self-attention mechanisms can revolutionize human motion prediction by effectively capturing these intricate patterns, leading to more accurate predictions. Future research directions should focus on enhancing model accuracy, exploring new techniques for capturing complex patterns, and enabling real-time implementation in wearable devices or smart environments. By addressing these areas, fall detection systems can be significantly improved, benefiting individuals and healthcare systems worldwide.