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)
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.