Model for Location and Action Recognition of Indoor Occupant in Office Buildings Based on PIR Sensor Array
The accurate identification of occupant behavior is the foundation of smart workspace.This study employed a passive infrared(PIR)sensor array to monitor occupant in the scene and analyzes the data characteristics of individuals at different positions and action intensity combinations.A recognition model was established utilizing multiple machine learning algorithms.Compared with single models such as KNN,RF,SVM,and MLP,the Stacking fusion model demonstrates superior performance and stability.When confronted with continuous data streams,the Stacking model with multiple time step combinations outperforms others.Considering overall indicators,short time step combination models are recommended;considering identification accuracy,long time step combination models with higher action intensity recognition accuracy are recommended.The model achieves an identification accuracy of 0.99,occupant location identification accuracy of 0.87,and occupant action intensity identification accuracy of 0.89.Then,the impact of region division and instrument layout on the model's performance was discussed.Using large-scale partitioning,the accuracy of occupant location recognition was improved;under small-scale partitioning,the location of occupant is more precise.The number of sensor stations can be reduced to eight by adjusting the spatial layout of PIR sensors.
occupant behaviorPIR sensoroccupant action recognitionmachine learningoccupant position
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