  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.
