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Abstract:
When monitoring elders’ daily routines, it is desirable to detect aberrant activity trends, as they may foreshadow a need for medical attention. But, traditional, unsupervised pattern classification techniques are ill-suited for this task, because the data distributions formed by the captured patterns are temporal in nature. To overcome this algorithmic deficit, we craft a framework for analyzing and displaying additive trends in feature data extracted from passive sensors.