A Comparison of Feature Extraction Methods Applied to Thermal Sensor Binary Image Data to Classify Bed Occupancy

Low-resolution thermal sensing technology is suitable for sleep monitoring due to being light invariant and privacy preserving. Feature extraction is a critical step in facilitating robust detection and tracking, therefore this paper compares a blob analysis approach of extracting statistical features to several common feature descriptor algorithm approaches (SURF and KAZE). The features are extracted from thermal binary image data for the purpose of detecting bed occupancy. Four common machine learning models (SVM, KNN, DT and NB) were trained and evaluated using a leave-one-subject-out validation method. The SVM trained with feature descriptor data achieved the highest accuracy of 0.961.