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

  • Amanda D. P. Venceslau Universidade Federal do Ceará (UFC)
  • 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)


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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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: