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Dressler, D., Liapota, P. & Löwe, W. (2019). Data Driven Human Movement Assessment. In: Ireneusz Czarnowski; Robert Howlett; Lakhmi C. Jain (Ed.), Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 2. Paper presented at 11th International KES Conference on Intelligent Decision Technologies, 17–19 June 2019, Malta (pp. 317-327). Springer
Open this publication in new window or tab >>Data Driven Human Movement Assessment
2019 (English)In: Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 2 / [ed] Ireneusz Czarnowski; Robert Howlett; Lakhmi C. Jain, Springer, 2019, p. 317-327Conference paper, Published paper (Refereed)
Abstract [en]

Quality assessment of human movements has many of applications in diagnosis and therapy of musculoskeletal insufficiencies and high performance sport. We suggest five purely data driven assessment methods for arbitrary human movements using inexpensive 3D sensor technology. We evaluate their accuracy by comparing them against a validated digitalization of a standardized human-expert-based assessment method for deep squats. We suggest the data driven method that shows high agreement with this baseline method, requires little expertise in the human movement and no expertise in the assessment method itself. It allows for an effective and efficient, automatic and quantitative assessment of  arbitrary human movements.

Place, publisher, year, edition, pages
Springer, 2019
Series
Smart Innovation, Systems and Technologies, ISSN 2190-3018 ; 143
National Category
Computer Sciences
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80984 (URN)10.1007/978-981-13-8303-8_29 (DOI)978-981-13-8302-1 (ISBN)
Conference
11th International KES Conference on Intelligent Decision Technologies, 17–19 June 2019, Malta
Note

Invited session on Digital Health, Distance Learning and decision support for eHealth

Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-08-28Bibliographically approved
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. In: Dr. David Shepherd (Ed.), QRS 2019 Proceedings: . Paper presented at The 19th IEEE International Conference on Software Quality, Reliability, and Security, July 22-26, 2019, Sofia, Bulgaria.
Open this publication in new window or tab >>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities
2019 (English)In: QRS 2019 Proceedings / [ed] Dr. David Shepherd, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Commit classification, the automatic classification of the purpose of changes to software, can support the understanding and quality improvement of software and its development process. We introduce code density of a commit, a measure of the net size of a commit, as a novel feature and study how well it is suited to determine the purpose of a change. We also compare the accuracy of code-density-based classifications with existing size-based classifications. By applying standard classification models, we demonstrate the significance of code density for the accuracy of commit classification. We achieve up to 89% accuracy and a Kappa of 0.82 for the cross-project commit classification where the model is trained on one project and applied to other projects. Such highly accurate classification of the purpose of software changes helps to improve the confidence in software (process) quality analyses exploiting this classification information.

Keywords
Software Quality, Commit Classification, Source Code Density, Maintenance Activities
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-85473 (URN)
Conference
The 19th IEEE International Conference on Software Quality, Reliability, and Security, July 22-26, 2019, Sofia, Bulgaria
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-09-26
Ulan, M., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Multi-criteria Ranking Based on Joint Distributions: A Tool to Support Decision Making. In: Pańkowska M., Sandkuhl K (Ed.), Perspectives in Business Informatics Research.BIR 2019: 18th International Conference on Business Informatics Research. Paper presented at 18th International Conference, BIR 2019, Katowice, Poland, September 23-25, 2019 (pp. 74-88). Springer
Open this publication in new window or tab >>Multi-criteria Ranking Based on Joint Distributions: A Tool to Support Decision Making
2019 (English)In: Perspectives in Business Informatics Research.BIR 2019: 18th International Conference on Business Informatics Research / [ed] Pańkowska M., Sandkuhl K, Springer, 2019, p. 74-88Conference paper, Published paper (Refereed)
Abstract [en]

Sound assessment and ranking of alternatives are fundamental to effective decision making. Creating an overall ranking is not trivial if there are multiple criteria, and none of the alternatives is the best according to all criteria. To address this challenge, we propose an approach that aggregates criteria scores based on their joint (probability) distribution and obtains the ranking as a weighted product of these scores. We evaluate our approach in a real-world use case based on a funding allocation problem and compare it with the traditional weighted sum aggregation model. The results show that the approaches assign similar ranks, while our approach is more interpretable and sensitive.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 365
Keywords
Aggregation, Management by objectives, Ranking
National Category
Software Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-89171 (URN)10.1007/978-3-030-31143-8_6 (DOI)978-3-030-31142-1 (ISBN)978-3-030-31143-8 (ISBN)
Conference
18th International Conference, BIR 2019, Katowice, Poland, September 23-25, 2019
Funder
Knowledge Foundation, 20150088
Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-09-18Bibliographically approved
Hagelbäck, J., Lincke, A., Löwe, W. & Rall, E. (2019). On the Agreement of Commodity 3D Cameras. In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA): . Paper presented at 3rd International Conference on Image Processing, Computer Vision, & Pattern Recognition. CSREA Press
Open this publication in new window or tab >>On the Agreement of Commodity 3D Cameras
2019 (English)In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA), CSREA Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitions of gold standards for each new movement. However, to train models for the automated assessments of a new movement they still need a lot of data that map recorded movements to expert judg- ments. As camera technology changes, this training needs to be repeated if a new camera does not agree with the old one. The present paper presents an inexpensive method to check the agreement of cameras, which, in turn, would allow for a safe reuse of trained models regardless of the cameras. We apply the method to the Kinect, Astra Mini, and Real Sense cameras. The results show that these cameras do not agree and that the models cannot be reused without an unacceptable decay in accuracy. However, the suggested method works independent of movements and cameras and could potentially save effort when integrating new cameras in an existing assessment environment.

Place, publisher, year, edition, pages
CSREA Press, 2019
Keywords
3D camera agreement, human movement assessment
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-89180 (URN)
Conference
3rd International Conference on Image Processing, Computer Vision, & Pattern Recognition
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18
Kopetschny, C., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Optimization of Software Estimation Models. In: Proceedings of the 14th International Conference on Software Technologies - Volume 1: . Paper presented at 14th International Conference on Software Technologies, ICSOFT, July 26-28, 2019, Prague, Czech Republic (pp. 141-150). SciTePress
Open this publication in new window or tab >>Optimization of Software Estimation Models
2019 (English)In: Proceedings of the 14th International Conference on Software Technologies - Volume 1, SciTePress, 2019, p. 141-150Conference paper, Published paper (Refereed)
Abstract [en]

In software engineering, estimations are frequently used to determine expected but yet unknown properties of software development processes or the developed systems, such as costs, time, number of developers, efforts, sizes, and complexities. Plenty of estimation models exist, but it is hard to compare and improve them as software technologies evolve quickly. We suggest an approach to estimation model design and automated optimization allowing for model comparison and improvement based on commonly collected data points. This way, the approach simplifies model optimization and selection. It contributes to a convergence of existing estimation models to meet contemporary software technology practices and provide a possibility for selecting the most appropriate ones.

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Estimation Models, Optimization, Software Engineering
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-88137 (URN)10.5220/0008117701410150 (DOI)978-989-758-379-7 (ISBN)
Conference
14th International Conference on Software Technologies, ICSOFT, July 26-28, 2019, Prague, Czech Republic
Funder
Knowledge Foundation, 20150088
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-28Bibliographically approved
Dressler, D., Liapota, P. & Löwe, W. (2019). Towards an automated assessment of musculoskeletal insufficiencies. In: Ireneusz Czarnowski; Robert Howlett; Lakhmi C. Jain (Ed.), Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 1. Paper presented at 11th International KES Conference on Intelligent Decision Technologies, 17–19 June 2019, Malta (pp. 251-261). Springer
Open this publication in new window or tab >>Towards an automated assessment of musculoskeletal insufficiencies
2019 (English)In: Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 1 / [ed] Ireneusz Czarnowski; Robert Howlett; Lakhmi C. Jain, Springer, 2019, p. 251-261Conference paper, Published paper (Refereed)
Abstract [en]

The paper suggests a quantitative assessment of human movements using inexpensive 3D sensor technology and evaluates its accuracy by comparing it with human expert assessments. The two assessment methods show a high agreement. To achieve this, a novel sequence alignment algorithm was developed that works for arbitrary time series.

Place, publisher, year, edition, pages
Springer, 2019
Series
Smart Innovation Systems and Technologies, ISSN 2190-3018 ; 142
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science; Computer and Information Sciences Computer Science, Computer Science; Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80946 (URN)10.1007/978-981-13-8311-3_22 (DOI)978-981-13-8310-6 (ISBN)
Conference
11th International KES Conference on Intelligent Decision Technologies, 17–19 June 2019, Malta
Note

Invited session on Data Selection in Machine Learning

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-08-28Bibliographically approved
Hagelbäck, J., Liapota, P., Lincke, A. & Löwe, W. (2019). Variants of Dynamic Time Warping and their Performance in Human Movement Assessment. In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA): . Paper presented at 21st International Conference on Artificial Intelligence, ICAI'19: July 29 - August 1, 2019, las Vegas, USA. CSREA Press
Open this publication in new window or tab >>Variants of Dynamic Time Warping and their Performance in Human Movement Assessment
2019 (English)In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA), CSREA Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements. However, there are many variations in selecting the approaches and setting the parameters for achieving good performance, i.e., high scoring accuracy and low response time. The present paper researches the design space and the impact of sequence alignment on accuracy and response time. More specifically, we introduce variants of Dynamic Time Warping (DTW) for aligning the phases of slow and fast movement instances and assess their effect on the scoring accuracy and response time. Results show that an automated stripping of leading and trailing frames not belonging to the movement (using one DTW variant) followed by an alignment of selected frames in the movements (based on another DTW variant) outperforms the original DTW and other suggested variants thereof. Since these results are independent of the selected learning approach and do not rely on the movement specifics, the results can help improving the performance of automated human movement assessment, in general.

Place, publisher, year, edition, pages
CSREA Press, 2019
Keywords
Dynamic Time Warping variants, human movement assessment
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-89181 (URN)
Conference
21st International Conference on Artificial Intelligence, ICAI'19: July 29 - August 1, 2019, las Vegas, USA
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2018). A changeset-based approach to assess source code density and developer efficacy. In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings: . Paper presented at 40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN (pp. 220-221). IEEE
Open this publication in new window or tab >>A changeset-based approach to assess source code density and developer efficacy
2018 (English)In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE , 2018, p. 220-221Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The productivity of a (team of) developer(s) can be expressed as a ratio between effort and delivered functionality. Several different estimation models have been proposed. These are based on statistical analysis of real development projects; their accuracy depends on the number and the precision of data points. We propose a data-driven method to automate the generation of precise data points. Functionality is proportional to the code size and Lines of Code (LoC) is a fundamental metric of code size. However, code size and LoC are not well defined as they could include or exclude lines that do not affect the delivered functionality. We present a new approach to measure the density of code in software repositories. We demonstrate how the accuracy of development time spent in relation to delivered code can be improved when basing it on net-instead of the gross-size measurements. We validated our tool by studying ca. 1,650 open-source software projects.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Keywords
Software Repositories, Clone Detection, Source code density, Effort estimation
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-79016 (URN)10.1145/3183440.3195105 (DOI)000450109000080 ()2-s2.0-85049691648 (Scopus ID)978-1-4503-5663-3 (ISBN)
Conference
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-08-29Bibliographically approved
Weyns, D., Ericsson, M., Löwe, W., Frejdestedt, F., Thornadtsson, J. & Hulth, A.-K. (2018). Applying Self-Adaptation to Automate the Management of Online Documentation of Telecom Systems. In: 14th International Conference on Automation Science and Engineering (CASE): Munich, Germany, August 20-24, 2018. Paper presented at 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 20-24, 2018 (pp. 1375-1380). IEEE
Open this publication in new window or tab >>Applying Self-Adaptation to Automate the Management of Online Documentation of Telecom Systems
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2018 (English)In: 14th International Conference on Automation Science and Engineering (CASE): Munich, Germany, August 20-24, 2018, IEEE, 2018, p. 1375-1380Conference paper, Published paper (Refereed)
Abstract [en]

Engineering software-intensive systems, such as production systems, is complex as these systems are subject to various types of changes that are often difficult to anticipate before deployment. Tackling this complexity requires joint expertise from different backgrounds. In this paper we focus on the problem of maintaining online technical documentation of telecom systems. In the context of continuous deployment and ever-changing user needs, high quality of the documentation of such products is in a key concern of users. To tackle this problem, different experts worked together equipping the online documentation system with a feedback loop. This feedback loop tracks changes in the system and its context and automatically adapts the documentation accordingly. The results demonstrate that this self-adaptation approach offers a viable solution to tackle the maintainability problem of online documentation of telecom systems.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80973 (URN)10.1109/COASE.2018.8560502 (DOI)978-1-5386-3593-3 (ISBN)
Conference
14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 20-24, 2018
Funder
Knowledge Foundation, 20150088
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-14Bibliographically approved
Ambrosius, R., Ericsson, M., Löwe, W. & Wingkvist, A. (2018). Interviews Aided with Machine Learning. In: Zdravkovic J., Grabis J., Nurcan S., Stirna J. (Ed.), Perspectives in Business Informatics Research. BIR 2018: 17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018, Proceedings. Paper presented at 17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018 (pp. 202-216). Springer, 330
Open this publication in new window or tab >>Interviews Aided with Machine Learning
2018 (English)In: Perspectives in Business Informatics Research. BIR 2018: 17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018, Proceedings / [ed] Zdravkovic J., Grabis J., Nurcan S., Stirna J., Springer, 2018, Vol. 330, p. 202-216Conference paper, Published paper (Refereed)
Abstract [en]

We have designed and implemented a Computer Aided Personal Interview (CAPI) system that learns from expert interviews and can support less experienced interviewers by for example suggesting questions to ask or skip. We were particularly interested to streamline the due diligence process when estimating the value for software startups. For our design we evaluated some machine learning algorithms and their trade-offs, and in a small case study we evaluates their implementation and performance. We find that while there is room for improvement, the system can learn and recommend questions. The CAPI system can in principle be applied to any domain in which long interview sessions should be shortened without sacrificing the quality of the assessment.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Business Information Processing ; 330
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80974 (URN)10.1007/978-3-319-99951-7_14 (DOI)2-s2.0-85054349611 (Scopus ID)978-3-319-99950-0 (ISBN)978-3-319-99951-7 (ISBN)
Conference
17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018
Funder
Knowledge Foundation, 20150088
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7565-3714

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