Unsupervised routine discovery in egocentric photo-streams
This work addresses health monitoring by analyzing daily routines from wearable camera data, but it is incremental as it applies an existing outlier detection approach to a new domain.
The paper tackled the problem of recognizing routine-related days from egocentric photo-streams using an unsupervised outlier detection model, achieving 76% accuracy and 68% weighted F-score across 72 days from 5 users.
The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person's health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people.