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In this work, we propose HAR-GCCN, a deep graph CNN model This implicit chronology can be used to learn unknown labels and classifyįuture activities. For example, we take a shower after we exercise. Likelihood of incorrect annotation or lack thereof, there is often an inherentĬhronology to human behavior. Interfere with the subjects' normal activities in real-time. Acquiring balanced datasets containingĪccurate activity labels requires humans to correctly annotate and potentially A critical challenge for training human activity Multiple domains, such as health monitoring, personal fitness, daily life Authors: Abduallah Mohamed, Fernando Lejarza, Stephanie Cahail, Christian Claudel, Edison Thomaz Download PDF Abstract: The problem of human activity recognition from mobile sensor data applies to

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