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Tools and Methods for Autonomous Analysis of Human Activities from Wearable Device Sensor Data


AutoActive is motivated by the need for better tools, methods and algorithms allowing extraction of reliable and useful information on human activity from heterogeneous sensor data. Present commercial systems can mainly give a direct mapping between a single measurement device and single parameters, for example GPS --> location and speed, and heart rate --> effort. However, to design and optimize such sensor solutions and algorithms is a long, empirical process that requires a broad range of competence. Access to this new level of information rely on - A physical and physiological understanding of the context and underlying processes, - A careful selection and combination of sensor devices - Data interpretation algorithms taking advantage of state-of-art data mining, machine learning and other multiparameter analysis methods. An open source software platform will be realised and used throughout the project to develop knowledge on how to collect and interpret data from multiple wearable sensor streams. General project results will be applied in two case studies to verify the project approach and methodology, as well as to demonstrate the potential of the technology. One case will be devoted to performance and technique assessment in sports, and one will be devoted to disease management for patients with multiple sclerosis. The project unites a multidisciplinary research team with partners from NTNU, Olympiatoppen, OUS, MS Senteret Hakadal, UiO, and SINTEF (leader), and will educate one PhD, at least 5 M.Sc and 2 part time Post.Docs.

Project leader: Trine M. Seeberg

Started: 2017

Ends: 2022

Category: Teknisk-industrielle institutter

Sector: Instituttsektor

Budget: 1.6e+07

Institution: Smart Sensor Systems

Address: Trondheim