Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies

Automatic Ingestion Monitor v2 (AIM-2) is an egocentric camera and sensor that aids monitoring of individual diet and eating behavior by capturing still images throughout the day and using sensor data to detect eating. The images may be used to recognize foods being eaten, eating environment, and other behaviors and daily activities. At the same time, captured images may carry privacy concerning content such as (1) people in social eating and/or bystanders (i.e., bystander privacy); (2) sensitive documents that may appear on a computer screen in the view of AIM-2 (i.e., context privacy). In this paper, we propose a novel approach based on automatic, image redaction for privacy protection by selective content removal by semantic segmentation using a deep learning neural network. The proposed method reported a bystander privacy removal with precision of 0.87 and recall of 0.94 and reported context privacy removal by precision and recall of 0.97 and 0.98. The results of the study showed that selective content removal using deep learning neural network is a much more desirable approach to address privacy concerns for an egocentric wearable camera for nutritional studies.

Orientation-Based Food Image Capture for Head Mounted Egocentric Camera

Current head-mounted wearable sensors for monitoring of food intake operates by fusing multiple modalities such as inertial and image sensing. The image capture may be performed periodically, capturing a large number of irrelevant images, increasing power consumption and reducing the battery life. In this manuscript, we propose an efficient approach for food image capture, that captures the images only when the head tilt angle estimated from the accelerometer data matches that during ingestion of food. The method was developed and validated using data from 15 volunteers consuming unrestricted meals in a free-living environment between 12.5 to 18.5 hours. The tilt angle of the head was computed using 3D accelerometer data. A classifier for image capture was developed using a curve fitting approach on the tilt angles of the head. The proposed method achieved a sensitivity of 0.97 and specificity of 0.47 in predicting capture of food images, thus potentially improving the battery life of the wearable device.