IEEE Trans Pattern Anal Mach Intell - Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors.

Tópicos

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Resumo

This paper considers scalable and unobtrusive activity recognition using on-body sensing for context-awareness in wearable computing. Common methods for activity recognition rely on supervised learning requiring substantial amounts of labeled training data. Obtaining accurate and detailed annotations of activities is challenging preventing the applicability of these approaches in real-world settings. This paper proposes new annotation strategies that substantially reduce the required amount of annotation. We explore two learning schemes for activity recognition that effectively leverage such sparsely labeled data together with more easily obtainable unlabeled data. Experimental results on two public datasets indicate that both approaches obtain results close to fully supervised techniques. The proposed methods are robust to the presence of erroneous labels occurring in real world annotation data.

Resumo Limpo

paper consid scalabl unobtrus activ recognit use onbodi sens contextawar wearabl comput common method activ recognit reli supervis learn requir substanti amount label train data obtain accur detail annot activ challeng prevent applic approach realworld set paper propos new annot strategi substanti reduc requir amount annot explor two learn scheme activ recognit effect leverag spars label data togeth easili obtain unlabel data experiment result two public dataset indic approach obtain result close fulli supervis techniqu propos method robust presenc erron label occur real world annot data

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