Prof. Xingda Qu
Shenzhen University
Talk Title
Human-Centered Intelligent Systems for Fall Prevention: A Human Factors and Ergonomics Perspective
Abstract
Falls are a leading cause of injuries and fatalities worldwide, particularly among older adults and individuals working in physically demanding environments. Effective fall prevention remains challenging because fall risk arises from complex interactions among sensory, physical, and cognitive factors during human movement and interaction with the environment. In this talk, Prof. Qu presents recent advances in understanding and preventing falls from a human factors and ergonomics perspective, and discusses how such knowledge can inform the design of intelligent human-centered technologies. Through a series of experimental and modeling studies, his research investigates how sensory, physical, and cognitive factors influence postural control, gait stability, and fall risk. These findings provide new insights into the mechanisms underlying accidental falls and establish a scientific basis for technology-assisted fall prevention. Building on these insights, the talk introduces several human-centered intelligent intervention technologies, including wearable sensing systems for fall risk assessment, individualized gait pattern prediction models for lower-limb exoskeleton control, and soft robotic assistance designed to improve gait stability. These approaches demonstrate how integrating biomechanics, wearable sensing, and artificial intelligence can support adaptive and personalized interventions, ultimately contributing to safer mobility and more reliable intelligent assistive systems.