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IKTPLUSS-IKT og digital innovasjon

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

Alternative title: Verktøy og metoder for autonom dataanalyse av menneskelig aktivitet med kroppsbårne sensorer

Awarded: NOK 16.0 mill.

AutoActive aims to develop tools, methods and algorithms for research on human activities based on data from multiple sensors. The project results will be valuable in a wide range of applications and are explored in two specific cases: analysis of performance and technique within sports, with a focus on cross-country skiing, and chronic disease management, with a focus on patients with multiple sclerosis (MS). The project is led by SINTEF Digital, Department of Smart Sensor Systems, and involves research partners from 1) NTNU (Center for Elite Sports Research (SenTIF)), 2) Olympiatoppen, 3) Oslo University Hospital (Department of Neurology), 4) MS-Senteret Hakadal and 5) The University of Oslo (Digital signal processing and image analysis). The project has had challenges with personnel and shutdown related to the Covid-19 pandemic, but despite this now managed to complete all the planned data collections. The project has so far published ten scientific articles and many other publications are under development or in the review process. The AutoActive Research environment, consisting of ActivityPresenter, Matlab and Phyton toolboxes, has been developed and is released in GIT Hub. The environment can receive, synchronize and visualize time series sensor data and video. Regular meetings have been arranged between the developers and the reference group, with representatives from both user scenarios present, to ensure that the platform is developed in line with user requirements. In the sports case, four data collections have been completed on recreational and elite skiers in the laboratory at SenTIF, outdoors in Holmenkollen on roller skis and on snow in Meråker. These data provide the basis for multiple scientific publications. Several items have already been presented at four different conferences; the Nordic Winter Sport Conference in 2019, Olympiatoppen research conference in 2019, the European College of Sport Science conference (ECSS) in 2020 and Third Annual Symposium On Sport Sciences in Aalborg University in 2021. The project has also published several scientific papers in the journals Frontiers, Sensors, Plus One, Sports Biomechanics and International Journal of Sports Medicine. In addition, several other papers are under development or under peer review. A data collection has also been carried out on well-trained cyclists at Kristiania University College. This work has already been presented at the ECSS conference, and a scientific article is almost ready for submission. In the MS case, a large data collection including 55 patients and 20 healthy controls at the MS-Senteret Hakadal was completed in October 2021. Here, movement data was collected during several standard tests conducted at the start and the end of a rehabilitation stay. This data collection forms the basis for several scientific studies and preliminary results have already been published on the European Network for Rehabilitation in MS Conference (euRIMS) in 2020 and 2021. In addition, one scientific paper is under review and several others are under development.

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.

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IKTPLUSS-IKT og digital innovasjon

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