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Ortiz-Barrios, M., Nugent, C., Cleland, I., Donnelly, M. & Verikas, A. (2020). Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework. Journal of Multi-Criteria Decision Analysis, 27(1-2), 20-38
Open this publication in new window or tab >>Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework
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2020 (English)In: Journal of Multi-Criteria Decision Analysis, ISSN 1057-9214, E-ISSN 1099-1360, Vol. 27, no 1-2, p. 20-38Article in journal (Refereed) Published
Abstract [en]

The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self-management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision-making approach for the modelling of technology adoption. Considering the above-mentioned aspects, this paper presents the integration of a five-phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile-based self-management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k-nearest-neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research. © 2019 John Wiley & Sons, Ltd.

Place, publisher, year, edition, pages
Hoboken: John Wiley & Sons, 2020
Keywords
assistive technology, classifier, dementia, FAHPTOPSIS
National Category
Information Systems Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:hh:diva-43577 (URN)10.1002/mcda.1678 (DOI)000476461100001 ()2-s2.0-85068533222 (Scopus ID)
Note

Funding information: Research and Innovation Staff Exchange (RISE)program, Grant/Award Number: H2020‐MSCA‐RISE‐2016.

Available from: 2020-12-07 Created: 2020-12-07 Last updated: 2020-12-07Bibliographically approved
Khan, T., Lundgren, L., Anderson, D. G., Novak, I., Dougherty, M., Verikas, A., . . . Aharonson, V. (2019). Assessing Parkinson's disease severity using speech analysis in non-native speakers. Computer speech & language (Print), 61, Article ID 101047.
Open this publication in new window or tab >>Assessing Parkinson's disease severity using speech analysis in non-native speakers
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2019 (English)In: Computer speech & language (Print), ISSN 0885-2308, E-ISSN 1095-8363, Vol. 61, article id 101047Article in journal (Refereed) Published
Abstract [en]

Background: Speech disorder is a common manifestation of Parkinson's disease with two main symptoms, dysprosody and dysphonia. Previous research studying objective measures of speech symptoms involved patients and examiners who were native language speakers. Measures such as cepstral separation difference (CSD) features to quantify dysphonia and dysprosody accurately distinguish the severity of speech impairment. Importantly CSD, together with other speech features, including Mel-frequency coefficients, fundamental-frequency variation, and spectral dynamics, characterize speech intelligibility in PD. However, non-native language speakers transfer phonological rules of their mother language that tamper speech assessment.

Objectives: This paper explores CSD's capability: first, to quantify dysprosody and dysphonia of non-native language speakers, Parkinson patients and controls, and secondly, to characterize the severity of speech impairment when Parkinson's dysprosody accompanies non-native linguistic dysprosody.

Methods: CSD features were extracted from 168 speech samples recorded from 19 healthy controls, 15 rehabilitated and 23 not-rehabilitated Parkinson patients in three different clinical speech tests based on Unified Parkinson's disease rating scale motor-speech examination. Statistical analyses were performed to compare groups using analysis of variance, intraclass correlation, and Guttman correlation coefficient µ2. Random forests were trained to classify the severity of speech impairment using CSD and the other speech features. Feature importance in classification was determined using permutation importance score.

Results: Results showed that the CSD feature describing dysphonia was uninfluenced by non-native accents, strongly correlated with the clinical examination (µ2>0.5), and significantly discriminated between the healthy, rehabilitated, and not-rehabilitated patient groups based on the severity of speech symptoms. However, the feature describing dysprosody did not correlate with the clinical examination but significantly distinguished the groups. The classification model based on random forests and selected features characterized the severity of speech impairment of non-native language speakers with high accuracy. Importantly, the permutation importance score of the CSD feature representing dysphonia was the highest compared to other features. Results showed a strong negative correlation (µ2<-0.5) between L-dopa administration and the CSD features.

Conclusions: Although non-native accents reduce speech intelligibility, the CSD features can accurately characterize speech impairment, which is not always possible in the clinical examination. Findings support using CSD for monitoring Parkinson's disease.

© 2019 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
London, UK: Academic Press, 2019
Keywords
Dysphonia, Dysprosody, Parkinson's disease, Speech processing, Tele-monitoring
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:hh:diva-41003 (URN)10.1016/j.csl.2019.101047 (DOI)000514574000001 ()2-s2.0-85075748795 (Scopus ID)
Note

Funding: Promobilia Foundation, Sweden

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2022-05-12Bibliographically approved
Bouguelia, M.-R., Nowaczyk, S., Santosh, K. C. & Verikas, A. (2018). Agreeing to disagree: active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics, 9(8), 1307-1319
Open this publication in new window or tab >>Agreeing to disagree: active learning with noisy labels without crowdsourcing
2018 (English)In: International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, E-ISSN 1868-808X, Vol. 9, no 8, p. 1307-1319Article in journal (Refereed) Published
Abstract [en]

We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label y = h(x)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled. © Springer-Verlag Berlin Heidelberg 2017

Place, publisher, year, edition, pages
Heidelberg: Springer, 2018
Keywords
Active learning, Classification, Label noise, Mislabeling, Interactive learning, Machine learning, Data mining
National Category
Signal Processing Computer Systems Computer Sciences
Identifiers
urn:nbn:se:hh:diva-33365 (URN)10.1007/s13042-017-0645-0 (DOI)000438855100006 ()2-s2.0-85050140726 (Scopus ID)
Available from: 2017-02-27 Created: 2017-02-27 Last updated: 2020-02-03Bibliographically approved
Rimavičius, T., Gelžinis, A., Verikas, A., Vaiciukynas, E., Bačauskiene, M. & Šaškov, A. (2018). Automatic benthic imagery recognition using a hierarchical two-stage approach. Signal, Image and Video Processing, 12(6), 1107-1114
Open this publication in new window or tab >>Automatic benthic imagery recognition using a hierarchical two-stage approach
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2018 (English)In: Signal, Image and Video Processing, ISSN 1863-1703, E-ISSN 1863-1711, Vol. 12, no 6, p. 1107-1114Article in journal (Refereed) Published
Abstract [en]

The main objective of this work is to establish an automated classification system of seabed images. A novel two-stage approach to solving the image region classification task is presented. The first stage is based on information characterizing geometry, colour and texture of the region being analysed. Random forests and support vector machines are considered as classifiers in this work. In the second stage, additional information characterizing image regions surrounding the region being analysed is used. The reliability of decisions made in the first stage regarding the surrounding regions is taken into account when constructing a feature vector for the second stage. The proposed technique was tested in an image region recognition task including five benthic classes: red algae, sponge, sand, lithothamnium and kelp. The task was solved with the average accuracy of 90.11% using a data set consisting of 4589 image regions and the tenfold cross-validation to assess the performance. The two-stage approach allowed increasing the classification accuracy for all the five classes, more than 27% for the “difficult” to recognize “kelp” class. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

Place, publisher, year, edition, pages
London: Springer, 2018
Keywords
Seabed image segmentation, Machine learning, Supervised classification, Feature extraction, Two-stage classifier
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-38428 (URN)10.1007/s11760-018-1262-4 (DOI)000441392700011 ()2-s2.0-85051420102 (Scopus ID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2018-11-27Bibliographically approved
Vaiciukynas, E., Gelzinis, A., Verikas, A. & Bacauskiene, M. (2018). Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks. In: Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J. (Ed.), Smart objects and technologies for social good: Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings. Paper presented at Third EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017 (pp. 206-215). Cham: Springer, 233
Open this publication in new window or tab >>Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks
2018 (English)In: Smart objects and technologies for social good: Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings / [ed] Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J., Cham: Springer, 2018, Vol. 233, p. 206-215Conference paper, Published paper (Refereed)
Abstract [en]

Application of deep learning tends to outperform hand-crafted features in many domains. This study uses convolutional neural networks to explore effectiveness of various segments of a speech signal,? – text-dependent pronunciation of a short sentence, – in Parkinson’s disease detection task. Besides the common Mel-frequency spectrogram and its first and second derivatives, inclusion of various other input feature maps is also considered. Image interpolation is investigated as a solution to obtain a spectrogram of fixed length. The equal error rate (EER) for sentence segments varied from 20.3% to 29.5%. Fusion of decisions from sentence segments achieved EER of 14.1%, whereas the best result when using the full sentence exhibited EER of 16.8%. Therefore, splitting speech into segments could be recommended for Parkinson’s disease detection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, E-ISSN 1867-8211 ; 233
Keywords
Parkinson’s disease, Audio signal processing, Convolutional neural network, Information fusion
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-36617 (URN)10.1007/978-3-319-76111-4_21 (DOI)2-s2.0-85043599499 (Scopus ID)978-3-319-76111-4 (ISBN)
Conference
Third EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017
Note

Funding: Research Council of Lithuania (No. MIP-075/2015)

Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2020-02-03Bibliographically approved
Minelga, J., Verikas, A., Vaiciukynas, E., Gelzinis, A. & Bacauskiene, M. (2017). A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data. Applied Sciences: APPS, 7(10), 1-15, Article ID 1096.
Open this publication in new window or tab >>A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
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2017 (English)In: Applied Sciences: APPS, E-ISSN 1454-5101, Vol. 7, no 10, p. 1-15, article id 1096Article in journal (Refereed) Published
Abstract [en]

The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject’s data into ’healthy’ and ’pathological’ classes as well as visual exploration of data and automatic decisions. A set of association rules and a decision tree, techniques lending themselves for exploration, were generated for pathology detection. Data pairwise similarities, estimated in a novel way, were mapped onto a 2D metric space for visual inspection and analysis. Accurate identification of pathological cases was observed on unseen subjects using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice: equal error rate (EER) of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The developed solution can be a useful tool for Otolaryngology departments in diagnostics, education and exploratory tasks. © 2017 by the authors.

Place, publisher, year, edition, pages
Bucharest: Universitatea Politehnica din Bucuresti, 2017
Keywords
decision tree, t-SNE visualization, association rules, pathological voice
National Category
Medical Engineering
Identifiers
urn:nbn:se:hh:diva-35313 (URN)10.3390/app7101096 (DOI)000414457800134 ()2-s2.0-85032291253 (Scopus ID)
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2023-09-15Bibliographically approved
Vaiciukynas, E., Verikas, A., Gelzinis, A. & Bacauskiene, M. (2017). Detecting Parkinson's disease from sustained phonation and speech signals. PLOS ONE, 12(10), Article ID e0185613.
Open this publication in new window or tab >>Detecting Parkinson's disease from sustained phonation and speech signals
2017 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 12, no 10, article id e0185613Article in journal (Refereed) Published
Abstract [en]

This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson’s disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and smart phone (SP) microphones. Additional modalities were obtained by splitting speech recording into voiced and unvoiced parts. Information in each modality is summarized by 18 well-known audio feature sets. Random forest (RF) is used as a machine learning algorithm, both for individual feature sets and for decision-level fusion. Detection performance is measured by the out-of-bag equal error rate (EER) and the cost of log-likelihood-ratio. Essentia audio feature set was the best using the AC speech modality and YAAFE audio feature set was the best using the SP unvoiced modality, achieving EER of 20.30% and 25.57%, respectively. Fusion of all feature sets and modalities resulted in EER of 19.27% for the AC and 23.00% for the SP channel. Non-linear projection of a RF-based proximity matrix into the 2D space enriched medical decision support by visualization. © 2017 Vaiciukynas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Place, publisher, year, edition, pages
San Francisco, CA: Public Library of Science, 2017
Keywords
Speech analysis, Pathology detection, Parkinson's disease
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-35229 (URN)10.1371/journal.pone.0185613 (DOI)000412360300047 ()28982171 (PubMedID)2-s2.0-85030766664 (Scopus ID)
Note

Funding: The Research Council of Lithuania (No. MIP-075/2015)

Available from: 2017-10-19 Created: 2017-10-19 Last updated: 2021-06-14Bibliographically approved
Verikas, A., Parker, J., Bacauskiene, M. & Olsson, M. C. (2017). Exploring relations between EMG and biomechanical data recorded during a golf swing. Expert systems with applications, 88, 109-117
Open this publication in new window or tab >>Exploring relations between EMG and biomechanical data recorded during a golf swing
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 88, p. 109-117Article in journal (Refereed) Published
Abstract [en]

Exploring relations between patterns of peak rotational speed of thorax, pelvis and arm, and patterns of EMG signals recorded from eight muscle regions of forearms and shoulders during the golf swing is the main objective of this article. The linear canonical correlation analysis, allowing studying relations between sets of variables, was the main technique applied. To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used. The experimental investigations using data from 16 golfers revealed statistically significant relations between sets of input and output variables. A strong direct linear relation was observed between lin- ear combinations of EMG averages and peak rotational speeds. The coefficient of determination values R2 = 0 . 958 and R2 = 0 . 943 obtained on unseen data by the random forest models designed to predict peak rotational speed of thorax and pelvis , indicate high modelling accuracy. However, predictions of peak rotational speed of arm were less accurate. This was expected, since peak rotational speed of arm played a minor role in the linear combination of peak speeds. The most important muscles to predict peak rotational speed of the body parts were identified. The investigations have shown that the canon- ical correlation analysis is a promising tool for studying relations between sets of biomechanical and EMG data. Better understanding of these relations will lead to guidelines concerning muscle engagement and coordination of thorax, pelvis and arms during a golf swing and will help golf coaches in providing substantiated advices. ©2017 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Kidlington, Oxford: Pergamon Press, 2017
Keywords
Canonical correlation, Random forest, Prediction, EMG, Golf
National Category
Sport and Fitness Sciences
Identifiers
urn:nbn:se:hh:diva-34611 (URN)10.1016/j.eswa.2017.06.041 (DOI)000408789300008 ()2-s2.0-85021670724 (Scopus ID)
Funder
Knowledge Foundation, 2012/0319
Available from: 2017-07-12 Created: 2017-07-12 Last updated: 2021-05-11Bibliographically approved
Ražanskas, P., Verikas, A., Viberg, P.-A. & Olsson, C. M. (2017). Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing. Biomedical Signal Processing and Control, 35, 19-29
Open this publication in new window or tab >>Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing
2017 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, p. 19-29Article in journal (Refereed) Published
Abstract [en]

This article is concerned with a novel technique for prediction of blood lactate concentration level and oxygen uptake rate from multi-channel surface electromyography (sEMG) signals. The approach is built on predictive models exploiting a set of novel time-domain variables computed from sEMG signals. Signals from three muscles of each leg, namely, vastus lateralis, rectus femoris, and semitendinosus were used in this study. The feature set includes parameters reflecting asymmetry between legs, phase shifts between activation of different muscles, active time percentages, and sEMG amplitude. Prediction ability of both linear and non-linear (random forests-based) models was explored. The random forests models showed very good prediction accuracy and attained the coefficient of determination R2 = 0.962 for lactate concentration level and R2 = 0.980 for oxygen uptake rate. The linear models showed lower prediction accuracy. Comparable results were obtained also when sEMG amplitude data were removed from the training sets. A feature elimination algorithm allowed to build accurate random forests (R2 > 0.9) using just six (lactate concentration level) or four (oxygen uptake rate) time-domain variables. Models created to predict blood lactate concentration rate relied on variables reflecting interaction between front and back leg muscles, while parameters computed from front muscles and interactions between two legs were the most important variables for models created to predict oxygen uptake rate.© 2017 Elsevier Ltd.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2017
Keywords
Random forests, Surface electromyography, Muscle activation patterns, Fatigue detection, Bicycling
National Category
Medical Engineering
Identifiers
urn:nbn:se:hh:diva-33966 (URN)10.1016/j.bspc.2017.02.011 (DOI)000401209300003 ()2-s2.0-85014392704 (Scopus ID)
Available from: 2017-06-03 Created: 2017-06-03 Last updated: 2017-06-09Bibliographically approved
Menezes, M. L., Samara, A., Galway, L., Pinheiro Sant'Anna, A., Verikas, A., Alonso-Fernandez, F., . . . Bond, R. (2017). Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset. Personal and Ubiquitous Computing, 21(6), 1003-1013
Open this publication in new window or tab >>Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset
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2017 (English)In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 21, no 6, p. 1003-1013Article in journal (Refereed) Published
Abstract [en]

One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).

Place, publisher, year, edition, pages
London: Springer London, 2017
Keywords
Classification (of information), Decision trees, Electroencephalography, Feature extraction, Speech recognition, Virtual reality, Affective Computing, Affective state, Benchmark datasets, Circumplex models, Classification accuracy, Electroencephalogram signals, Emotion recognition, Interaction experiences, Behavioral research
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-36499 (URN)10.1007/s00779-017-1072-7 (DOI)000416170900005 ()2-s2.0-85027845103 (Scopus ID)
Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2020-05-11Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-2185-8973

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