UCLA

Recent advancements in mobile health applications of human motion sensing systems have enabled the proliferation of low-cost solutions in health and athletics assessment. As sensor data analysis and systems become more sophisticated the next innovation in this field is to provide user guidance towards improved outcomes. To this end, physiology research in distance running provides the framework to compute physics-based metrics to characterize running performance. This thesis presents a novel running gait cycle characterization system to monitor the race performance of elite runners based on several monotonic, computationally light metrics and an overall efficiency metric. The presented real time race results demonstrate the ability of motion sensing algorithms and systems to monitor, guide and enhance runner performance.