SeAct: Semantic Adaptive Segmentation of Sensor Data Streams for Human Activity Recognition

  • Amanda D. P. Venceslau Universidade Federal do Ceará (UFC) http://orcid.org/0000-0003-4118-4224
  • Vânia M. P. Vidal Universidade Federal do Ceará (UFC)
  • Rossana M. C. Andrade Universidade Federal do Ceará (UFC)
  • José Gilvan R. Maia Universidade Federal do Ceará (UFC)
  • José Wellington F. da Silva Universidade Federal do Ceará (UFC)

Resumo


Pervasive computing delivers services based on user needs through smart environments that incorporate and integrate everyday objects discreet and non-intrusive. Personal applications provide the data collected by sensors for Human Activity Recognition. The main limitation is that these activities need to be continuously segmented for HAR. Furthermore, a growing problem is related to the disambiguation of activities since some actions generated by the same sensors belong to different activities. This paper proposes a hybrid method, SeAct, which dynamically adjusts segment size, combining machine learning and semantic inference. Experiments with CAD-120 data sets and a state-of-the-art hybrid method improve recognition accuracy and precision.

Palavras-chave: Data Segmentation, Human Activity Recognition, Hybrid Method, Machine Learning and Semantic Inference

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Publicado
19/09/2022
VENCESLAU, Amanda D. P.; VIDAL, Vânia M. P.; ANDRADE, Rossana M. C.; MAIA, José Gilvan R.; F. DA SILVA, José Wellington. SeAct: Semantic Adaptive Segmentation of Sensor Data Streams for Human Activity Recognition. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 317-329. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2022.225042.