Integrating prior Knowledge in Weighted SVM for Human Activity Recognition in Smart Home

International audienceFeature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we performed a new hybrid model using Temporal or Spatial Features (TF or SF) with the PCA-LDA-WSVM classifier. The last method combines two methods for feature extraction: Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) followed by Weighted SVM Classifier. This classifier is used to handle the problem of imbalanced activity data from sensor readings. The experiments that were implemented on multiple real-world datasets, showed the effectiveness of TF and SF attributes combined with PCA-LDA-WSVM in activity recognition

Integrating prior Knowledge in Weighted SVM for Human Activity Recognition in Smart Home

International audienceFeature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we performed a new hybrid model using Temporal or Spatial Features (TF or SF) with the PCA-LDA-WSVM classifier. The last method combines two methods for feature extraction: Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) followed by Weighted SVM Classifier. This classifier is used to handle the problem of imbalanced activity data from sensor readings. The experiments that were implemented on multiple real-world datasets, showed the effectiveness of TF and SF attributes combined with PCA-LDA-WSVM in activity recognition