Scalable Recognition of Human Activities for Pervasive Applications in Natural Environments


August 1, 2014


Past approaches on the automatic recognition of human activities have achieved promising results by sensing patterns of physical motion via wireless accelerometers worn on the body and classifying them using supervised or semi-supervised machine learning algorithms. Despite their relative success, once moving beyond demonstrators, these approaches are limited by several problems. For instance, they don’t adapt to changes caused by addition of new activities or variations in the environment; they don’t accommodate the high variability produced by the disparity in how activities are performed across users; and they don’t scale up to a large number of users or activities. The solution to these fundamental problems is critical for systems intended to be deployed in natural settings, particularly, for those that require long-term deployment at a large-scale.

This talk discusses these problems and presents an activity recognition framework using an incremental learning paradigm. The proposed framework allows learning new activities – or more examples of existing activities – in an incremental manner without requiring the entire model to be retrained, it effectively handles within-user variations, and it is able to transfer knowledge among activities and users. It also presents a functional system based on the framework presented which was designed and implemented across a variety of application scenarios (from a social-exergame for children to a long-term data collection of physical activities in free-living settings). Lessons learned from these practical implementations are summarized and discussed.


Selene Mota

Selene Mota is a Ph.D. candidate at Massachusetts Institute of Technology in the department of Design and Computation and Media Laboratory. Her research has an interdisciplinary orientation that involves four main areas of interest: behavioral analysis, machine learning, affective computing and user-centered design. Her master degree work is pioneering for being able to successfully characterize dynamic patterns of movements that read emotional states from body language. Her PhD is focused on creating large-scale physical activity recognition systems that can be deployed on common mobile phones for monitoring natural occurring behavior in fee-living settings.

Selene holds a Bachelors degree in Electrical Engineering with magna cum laude from Monterrey Institute of Technology in Mexico, and a Master in Media Arts and Sciences from the Massachusetts Institute of Technology. She was a top finalist at the MIT 100K Competition and the CIMIT Prize. She has collaborated with several research institutions such as the Swiss Institute of Technology in Lausanne, Philips Research in The Netherlands, and the German Institute of Artificial Intelligence.