Xiaolong Ma

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About

Xiaolong Ma is a researcher specializing in human motion analysis, fall detection, and sensor-based safety systems, with a particular focus on applications for elderly care and assistive technology. His most recognized contribution centers on developing advanced pre-impact fall detection methodologies, notably his 2018 work introducing a modified Zero Moment Point (ZMP) criterion leveraging Kinect sensor data — a study that has garnered 41 citations and demonstrated meaningful progress in extending the lead time available for protective interventions before a fall occurs. By adapting biomechanical stability principles traditionally applied in humanoid robotics to real-world human monitoring scenarios, Ma bridged the gap between theoretical motion analysis and practical healthcare applications. His research addresses a pressing global challenge, as accidental falls remain one of the leading causes of injury and mortality among older adults. Through innovative integration of depth-sensing technology and biomechanical modeling, Ma's work has contributed actionable solutions to fall prevention systems, earning recognition within the fields of biomedical engineering, computer vision, and human-computer interaction. His scholarship reflects a commitment to translating computational research into meaningful improvements in quality of life for vulnerable populations.