Machine Learning Model of Indoor Occupant Behavior Based on Passive Infrared Sensor

Abstract:

Accurate recognition of indoor occupant behavior， including the recognition of position and activity type， is an important input for multi-scene control mode of various electrical equipment at intelligent homes. In the study described in this paper， the passive infrared （PIR） sensor array is used to monitor indoor occupant behavior. After analyzing the data characteristics of different positions and different activity intensities， based on the machine learning algorithm， the indoor occupant position and activity recognition model is established and the recognition accuracy of different cumulative time and machine learning algorithms are compared. The cumulative count value of this minute and of the previous 30 minutes of the PIR sensors are selected as the model input and the random forest algorithm is used to construct the final position and activity recognition model. The accuracy of the model is 99.9% under the 10-fold cross-validation of the training data set， while 88.3% under the new test data set， which shows that the position and activity recognition model has a certain validity and generality.