Abstract

We introduce novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary technical contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection, particularly for different public transportation modalities. As part of the talk, I will also present an overview of the activities of the Ubiquitous Sensing research group at University of Helsinki.

Bio: Petteri Nurmi is a Senior Researcher at University of Helsinki and Helsinki Institute for Information Technology HIIT where he is leading the Ubiquitous Sensing research group. He received his PhD in 2009 from the Department of Computer Science in University of Helsinki and is a Docent of Computer Science since 2012. His research interests include mobile sensing, adaptive interfaces, and ubiquitous computing in general.