Recognizing Activities of Daily Living from Egocentric Images
This work addresses health applications like habit improvement and rehabilitation by improving activity recognition from wearable camera data, but it is incremental as it builds on existing CNN methods.
The paper tackled the problem of recognizing Activities of Daily Living from low-resolution egocentric images using a CNN combined with contextual information via a random forest, achieving 86% accuracy on 21 classes.
Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes.