Automated Detection of Hands and Objects in Egocentric Videos, for Ambient Assisted Living Applications

The need for technology assisted (or ambient assisted) living is increasing all the time as the population ages and the number of people with dementia and other conditions impairing memory and cognitive ability increases. In such applications, amongst others, it is necessary to identify and assess potentially hazardous situations. These include scenarios involving a person's hands and their interactions with various objects. In this paper, we describe our novel approach to identify human hands and objects in videos of people performing a variety of everyday tasks. We compare the performance of our method using different strategies with that of other state of the art approaches. We conclude that, when the proposed approach takes advantage of a pre-trained model, hand detection is performed accurately (94%), providing reliable information for assisted living applications.