Food Intake Vision-Based Recognition System via Histogram of Oriented Gradients and Support Vector Machine for Persons With Alzheimer's Disease

Abstract

Due to cognitive decline, individuals with Alzheimer’s often suffer from malnutrition, forgetting to eat, even if food is presented. Therefore, assistance with feeding is needed. In this paper a vision-based system for monitoring of eating patterns is presented. Upper Body Region (UBR) is detected using Viola-Jones method, a histogram of oriented gradients (HOG) is generated for feature extraction, and a support vector machine (SVM) is used to distinguish eating versus non-eating. To reduce false positive results, Haar-like features are used to detect hands while moving between served food and mouth within the identified upper body region (UBR). A combined template image (CTI) method is proposed in this work to eliminate false positive hand detections where 30 hand eating posture images have been selected and combined into one template image. Matching implemented using CTI is 2.86 times faster than matching the subject to the 30 images separately. Experimental simulation used 33 videos of 163840 frames indicates that the proposed method achieves a high accuracy of 90.65%.