M.Sc. Samuli Hemminki defends his doctoral thesis Advances in Motion Sensing on Mobile Devices on Monday the 25th of November 2019 at 12 o'clock noon in the University of Helsinki Athena building, Lecture hall 302 (Siltavuorenpenger 3 A, 3rd floor). His opponent is Professor Youssef Moustafa (University of Alexandria, Egypt) and custos Associate Professor Petteri Nurmi (University of Helsinki). The defence will be held in English.
The thesis of Samuli Hemminki is a part of research done in the Department of Computer Science and in the Pervasive Data Science research group at the University of Helsinki. His supervisors have been Associate Professor Petteri Nurmi and Professor Sasu Tarkoma (University of Helsinki).
Advances in Motion Sensing on Mobile Devices
Motion sensing is one of the most important sensing capabilities of mobile devices, enabling monitoring physical movement of the device and associating the observed motion with predefined activities and physical phenomena. The present thesis is divided into three parts covering different facets of motion sensing techniques. In the first part of this thesis, we present techniques to identify the gravity component within three-dimensional accelerometer measurements. Our technique is particularly effective in the presence of sustained linear acceleration events. Using the estimated gravity component, we also demonstrate how the sensor measurements can be transformed into descriptive motion representations, able to convey information about sustained linear accelerations. To quantify sustained linear acceleration, we propose a set of novel peak features, designed to characterize movement during mechanized transportation. Using the gravity estimation technique and peak features, we proceed to present an accelerometer-based transportation mode detection system able to distinguish between fine-grained automotive modalities.
In the second part of the thesis, we present a novel sensor-assisted method, crowd replication, for quantifying usage of a public space. As a key technical contribution within crowd replication, we describe construction and use of pedestrian motion models to accurately track detailed motion information. Fusing the pedestrian models with a positioning system and annotations about visual observations, we generate enriched trajectories able to accurately quantify usage of public spaces.
Finally in the third part of the thesis, we present two exemplary mobile applications leveraging motion information. As the first application, we present a persuasive mobile application that uses transportation mode detection to promote sustainable transportation habits. The second application is a collaborative speech monitoring system, where motion information is used to monitor changes in physical configuration of the participating devices.
Availability of the dissertation
An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-951-51-5598-6.
Printed copies will be available on request from Samuli Hemminki: email@example.com.