Clustering of human activities from emerging movements

A flocking based unsupervised mining approach


This paper is positioned in the well-established field of smart home. This area of research, highly multidisciplinary, has raised a lot of attention from researchers due to the broad real life application it could serve. One of them is the assistance of the cognitively impaired persons such as head trauma victims or persons afflicted by dementia (e.g.: Alzheimer’s disease). However, to propose powerful technological cognitive orthoses, the decades old challenge of human activity recognition must be addressed. In this paper, we propose a clustering method exploiting the Flocking algorithm for Activity of Daily Living (ADL) learning and recognition. In particular, our new method enables to both exploit events based data from the multimodal sensors of a smart home and qualitative spatial information extracted from a tracking method. Among the advantages of the method, the Flocking based algorithm does not require an initial number of clusters, unlike other clustering algorithms such as K-means. Two sets of real case scenarios were collected in our smart home laboratory, the LIARA. These sets were used to compare our method with traditional unsupervised algorithms and to evaluate the usefulness of the qualitative spatial information. The study shows that for traditional event based smart home data, the method outperforms the popular K-Means and Expectation-Maximization (EM) algorithms. Furthermore, the results indicate that not only the spatial data generalize, but it also further improve the performance regarding fine-grained ADLs recognition.

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Correspondence to Kevin Bouchard.

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Bouchard, K., Lapalu, J., Bouchard, B. et al. Clustering of human activities from emerging movements. J Ambient Intell Human Comput 10, 3505–3517 (2019).

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  • Clustering
  • Smart Home
  • Flocking
  • Unsupervised data mining