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Publications (4 of 4) Show all publications
Stöggl, T. L., Holst, A., Jonasson, A., Andersson, E. P., Wunsch, T., Norström, C. & Holmberg, H. C. (2014). Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone. Sensors, 14(11), 20589-20601
Open this publication in new window or tab >>Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone
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2014 (English)In: Sensors, E-ISSN 1424-8220, Vol. 14, no 11, p. 20589-20601Article in journal (Refereed) Published
Abstract [en]

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

Place, publisher, year, edition, pages
MDPI AG, 2014
Keywords
Algorithm, Collective classification, Gaussian filter, Individual classification, Machine learning, Markov chain, Smartphone, Accelerometers, Algorithms, Artificial intelligence, Learning systems, Markov processes, Signal encoding, Smartphones, Video recording, Accelerometer data, Automatic algorithms, Automatic classification, Automatic procedures, Collective classifications, Gaussian filters, Smart-phone applications, Variable protocols, Gears
National Category
Engineering and Technology
Identifiers
urn:nbn:se:ri:diva-45484 (URN)10.3390/s141120589 (DOI)2-s2.0-84908530187 (Scopus ID)
Available from: 2020-08-11 Created: 2020-08-11 Last updated: 2023-05-09Bibliographically approved
Ahlgren, B., Jonasson, A. & Ohlman, B. (2014). HTTP Live Streaming over NetInf Transport (6ed.). In: : . Paper presented at 1st ACM Conference on Information-Centric Networking (ICN) (pp. 203-204). Paris, France
Open this publication in new window or tab >>HTTP Live Streaming over NetInf Transport
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We modified a commercial Android TV app to use NetInf ICN transport. It was straightforward to adapt the standard HTTP Live Streaming to NetInf naming and network service. We demonstrate that NetInf's in-network caching and request aggregation result in efficient live TV distribution.

Place, publisher, year, edition, pages
Paris, France: , 2014 Edition: 6
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24451 (URN)10.1145/2660129.2660136 (DOI)2-s2.0-84942304165 (Scopus ID)
Conference
1st ACM Conference on Information-Centric Networking (ICN)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-10Bibliographically approved
Ahlgren, B. & Jonasson, A. (2014). Scalable Live TV Distribution with NetInf to Android Devices (poster/demo) (8ed.). In: : . Paper presented at 10th Swedish National Computer Networking Workshop (SNCNW). Västerås, Sweden
Open this publication in new window or tab >>Scalable Live TV Distribution with NetInf to Android Devices (poster/demo)
2014 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Västerås, Sweden: , 2014 Edition: 8
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24452 (URN)
Conference
10th Swedish National Computer Networking Workshop (SNCNW)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-10Bibliographically approved
Ekman, J., Holst, A. & Jonasson, A. (2011). Condition based maintenance of trains doors (7ed.). Kista, Sweden: Swedish Institute of Computer Science
Open this publication in new window or tab >>Condition based maintenance of trains doors
2011 (English)Report (Other academic)
Abstract [en]

As part of the project DUST financed by Vinnova, we have investigated whether event data generated on trains can be used for finding evidence of wear on train doors. We have compared the event data and maintenance reports relating to doors of Regina trains. Although some interesting relations were found, the overall result is that the information in event data about wear of doors is very limited.

Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science, 2011 Edition: 7
Series
SICS Technical Report, ISSN 1100-3154 ; 2011:15
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24012 (URN)
Projects
DUST
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3639-9637

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