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Interactive Techniques for Labeling Activities Of Daily Living to Assist Machine Learning
Lookup NU author(s)
Dr Thomas Ploetz
Thomaz E, Ploetz T, Essa I, Abowd G
Conference Proceedings (inc. Abstract)
International Workshop on Interactive Systems in Healthcare
Austin, Texas, USA
Year of Conference
5-10 May 2012
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Over the next decade, as healthcare continues its march away from the hospital and towards the home, logging and making sense of activities of daily living will play a key role in health modeling and life-long home care. Machine learning research has explored ways to automate the detection and quantification of these activities in sensor-rich environments. While we continue to make progress in developing practical and cost-effective activity sensing techniques, one large hurdle remains, obtaining labeled activity data to train activity recognition systems. In this paper, we discuss the process of gathering ground truth data with human participation for health modeling applications. In particular, we propose a criterion and design space containing five dimensions that we have identified as central to the characterization and evaluation of interactive labeling methods.
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