Recognition of Similar Activities Based on Activity Relationship

With the Internet of Things development, recognizing activities of daily living usually deploy many sensors to objects and environments. However, the sensor events can be triggered by many activities which decreases the accuracy or even fails of inference. In addition, a huge number of personalized activities takes up a lot of space resources and reduces the readability of model. Therefore, this paper adds the duration and period characteristics to improve the inference performance, then adopts the structural model to increase the expendability and standardization. We show that the similar activities inference accuracy has been improved by an average of 8 times. The operating time has been decreased by an average of 0.36 times. This inference method has the good performance and can be used in the future for activity recognition.