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Järpe, Eric
Publications (10 of 19) Show all publications
Khan, T., Lundgren, L., Järpe, E., Olsson, M. C. & Wiberg, P. (2019). A Novel Method for Classification of Running Fatigue Using Change-Point Segmentatio. Sensors, 19(21), Article ID 4729.
Open this publication in new window or tab >>A Novel Method for Classification of Running Fatigue Using Change-Point Segmentatio
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 21, article id 4729Article in journal (Refereed) Published
Abstract [en]

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running. © 2019 by the authors.

Place, publisher, year, edition, pages
Basel: MDPI, 2019
Keywords
surface-electromyography, blood lactate concentration, random forest, running, fatigue
National Category
Sport and Fitness Sciences
Identifiers
urn:nbn:se:hh:diva-41038 (URN)10.3390/s19214729 (DOI)31683532 (PubMedID)2-s2.0-85074441602 (Scopus ID)
Note

Funding:This research was funded by the Knowledge Foundation through the research profile Centre for Applied Intelligence Research (CAISR) at Halmstad University. It was partly co-funded by the company Swedish Adrenaline.

Available from: 2019-11-27 Created: 2019-11-27 Last updated: 2019-12-04Bibliographically approved
Khan, T., Lundgren, L., Järpe, E., Olsson, M. C. & Wiberg, P. (2019). A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation. Sensors, 19(21), Article ID 4729.
Open this publication in new window or tab >>A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
Show others...
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 21, article id 4729Article in journal (Refereed) Published
Abstract [en]

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Place, publisher, year, edition, pages
Basel: MDPI, 2019
Keywords
surface-electromyography, blood lactate concentration, random forest, running, fatigue
National Category
Sport and Fitness Sciences
Identifiers
urn:nbn:se:hh:diva-40834 (URN)10.3390/s19214729 (DOI)2-s2.0-85074441602 (Scopus ID)
Funder
Knowledge Foundation
Note

Other funder: Swedish Adrenaline.

Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-12-06Bibliographically approved
Cooney, M., Ong, L., Pashami, S., Järpe, E. & Ashfaq, A. (2019). Avoiding Improper Treatment of Dementia Patients by Care Robots. In: : . Paper presented at The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019.
Open this publication in new window or tab >>Avoiding Improper Treatment of Dementia Patients by Care Robots
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.

Keywords
care robot, therapy robot, dementia, ethics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-39448 (URN)
Conference
The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019
Funder
Knowledge Foundation, 20140220
Note

Funder: EU REMIND project (H2020-MSCA-RISE No 734355)

Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-10-09
Järpe, E. (2019). Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project.
Open this publication in new window or tab >>Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project
2019 (English)Report (Other (popular science, discussion, etc.))
Publisher
p. 20
National Category
Gerontology, specialising in Medical and Health Sciences Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39442 (URN)
Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-05-27Bibliographically approved
Ali Hamad, R., Järpe, E. & Lundström, J. (2018). Stability analysis of the t-SNE algorithm for human activity pattern data. In: : . Paper presented at The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018.
Open this publication in new window or tab >>Stability analysis of the t-SNE algorithm for human activity pattern data
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-38442 (URN)
Conference
The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018
Projects
SA3L
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-01-11Bibliographically approved
Vaske, C., Weckstén, M. & Järpe, E. (2017). Velody — A novel method for music steganography. In: 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP 2017): September 6-8, 2017, Paris, France. Paper presented at 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP 2017), Paris, France, September 6-8, 2017 (pp. 15-19). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Velody — A novel method for music steganography
2017 (English)In: 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP 2017): September 6-8, 2017, Paris, France, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 15-19Conference paper, Published paper (Refereed)
Abstract [en]

This study describes a new method for musical steganography utilizing the MIDI format. MIDI is a standard music technology protocol that is used around the world to create music and make it available for listening. Since no publicly available method for MIDI steganography has been found (even though there are a few methods described in the literature), the study investigates how a new algorithm for MIDI steganography can be designed so that it satisfies capacity and security criteria. As part of the study, a method for using velocity values to hide information in music has been designed and evaluated, during which the capacity of the method is found to be comparable with similar methods. In an audibility test, it is observed that audible impact on the music can not be distinguished at any reasonable significance level, which means that also a security criterion is met. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Algorithms, Security of data, Security systems, Steganography, Velocity, audibility, capacity, Embeddings, Information hiding technology, MIDI, music, Secret messages, teganography, Signal processing
National Category
Media and Communication Technology
Identifiers
urn:nbn:se:hh:diva-40213 (URN)10.1109/ICFSP.2017.8097052 (DOI)000425242400003 ()2-s2.0-85039897034 (Scopus ID)978-1-5386-1038-1 (ISBN)978-1-5386-1037-4 (ISBN)978-1-5386-1036-7 (ISBN)978-1-5386-1039-8 (ISBN)
Conference
2017 3rd International Conference on Frontiers of Signal Processing (ICFSP 2017), Paris, France, September 6-8, 2017
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28
Weckstén, M., Frick, J., Sjostrom, A. & Järpe, E. (2016). A Novel Method for Recovery from Crypto Ransomware Infections. In: 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings: . Paper presented at 2nd IEEE International Conference on Computer and Communications (ICCC), Oct 14-17, 2016, Chengdu, China (pp. 1354-1358). New York: IEEE
Open this publication in new window or tab >>A Novel Method for Recovery from Crypto Ransomware Infections
2016 (English)In: 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings, New York: IEEE, 2016, p. 1354-1358Conference paper, Published paper (Refereed)
Abstract [en]

Extortion using digital platforms is an increasing form of crime. A commonly seen problem is extortion in the form of an infection of a Crypto Ransomware that encrypts the files of the target and demands a ransom to recover the locked data. By analyzing the four most common Crypto Ransomwares, at writing, a clear vulnerability is identified; all infections rely on tools available on the target system to be able to prevent a simple recovery after the attack has been detected. By renaming the system tool that handles shadow copies it is possible to recover from infections from all four of the most common Crypto Ransomwares. The solution is packaged in a single, easy to use script. © 2016 IEEE.

Place, publisher, year, edition, pages
New York: IEEE, 2016
Series
IEEE International Conference on Computer Communications and Networks, ISSN 1095-2055
Keywords
component, crypto ransom ware, malware, recovery, extortion, network security
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-35642 (URN)10.1109/CompComm.2016.7924925 (DOI)000411576802046 ()2-s2.0-85020228603 (Scopus ID)978-1-4673-9026-2 (ISBN)
Conference
2nd IEEE International Conference on Computer and Communications (ICCC), Oct 14-17, 2016, Chengdu, China
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-08-28Bibliographically approved
Lundström, J., Järpe, E. & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert systems with applications, 55, 429-440
Open this publication in new window or tab >>Detecting and exploring deviating behaviour of smart home residents
2016 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed) Published
Abstract [en]

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2016
Keywords
Ambient assisted living, Random forests, Stochastic neighbour embedding, Markov chain, Intelligent environments
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
urn:nbn:se:hh:diva-30594 (URN)10.1016/j.eswa.2016.02.030 (DOI)000374811000033 ()2-s2.0-84960082873 (Scopus ID)
Projects
CAISR / SA3L
Funder
Knowledge Foundation, 2010/0271
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2018-03-22Bibliographically approved
Lundström, J., Synnott, J., Järpe, E. & Nugent, C. (2015). Smart Home Simulation using Avatar Control and Probabilistic Sampling. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops): . Paper presented at SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015 (pp. 336-341). Piscataway, NJ: IEEE Press
Open this publication in new window or tab >>Smart Home Simulation using Avatar Control and Probabilistic Sampling
2015 (English)In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 336-341Conference paper, Published paper (Refereed)
Abstract [en]

Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval. © IEEE 2015

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2015
Keywords
Avatars, Intelligent sensors, Smart homes, Data models, Software, Conferences
National Category
Signal Processing Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-27741 (URN)10.1109/PERCOMW.2015.7134059 (DOI)000380510900076 ()2-s2.0-84946097507 (Scopus ID)978-1-4799-8425-1 (ISBN)
Conference
SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015
Projects
SA3L, CAISR
Funder
Knowledge Foundation
Note

This work was supported by the Knowledge Foundation of Sweden, Grant Number 2010/0271. Additionally, Invest Northern Ireland is acknowledged for supporting this project under the Competence Centre Program Grant RD0513853 - Connected Health Innovation Centre.

Available from: 2015-05-26 Created: 2015-02-09 Last updated: 2018-03-22Bibliographically approved
Rögnvaldsson, T., Norrman, H., Byttner, S. & Järpe, E. (2014). Estimating p-Values for Deviation Detection. In: Randall Bilof (Ed.), Proceedings: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems SASO 2014. Paper presented at SASO 2014 - Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Imperial College, London, United Kingdom, September 8-12, 2014 (pp. 100-109). Los Alamitos, CA: IEEE Computer Society
Open this publication in new window or tab >>Estimating p-Values for Deviation Detection
2014 (English)In: Proceedings: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems SASO 2014 / [ed] Randall Bilof, Los Alamitos, CA: IEEE Computer Society, 2014, p. 100-109Conference paper, Published paper (Refereed)
Abstract [en]

Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only "normal" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple "most central pattern" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the "normal" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervisi- n and without prior knowledge about what is being monitored. © 2014 IEEE.

Place, publisher, year, edition, pages
Los Alamitos, CA: IEEE Computer Society, 2014
Series
International Conference on Self-Adaptive and Self-Organizing Systems : [proceedings], ISSN 1949-3673
Keywords
Training, Kernel, Vehicles, Conferences, Histograms, Design automation, Measurement
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-26151 (URN)10.1109/SASO.2014.22 (DOI)000361021200011 ()2-s2.0-84936889577 (Scopus ID)978-1-4799-5367-7 (ISBN)978-1-4799-5368-4 (ISBN)
Conference
SASO 2014 - Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Imperial College, London, United Kingdom, September 8-12, 2014
Funder
VINNOVA
Note

Funding: Vinnova & Volvo AB

Available from: 2014-07-15 Created: 2014-07-15 Last updated: 2018-03-22Bibliographically approved
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