SeAct: Semantic Adaptive Segmentation of Sensor Data Streams for Human Activity Recognition - Venceslau, Amanda D. P.; Vidal, Vânia M. P.; Andrade, Rossana M. C.; Maia, José Gilvan R.; F. da Silva, José Wellington (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2022: Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, 2022)
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.