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Memedi, Mevludin, PhDORCID iD iconorcid.org/0000-0002-2372-4226
Publications (10 of 48) Show all publications
Memedi, M., Aghanavesi, S., Bergquist, F., Nyholm, D. & Senek, M. (2019). A multimodal sensor fusion platform for objective assessment of motor states in Parkinson's disease. In: IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI 19): . Paper presented at IEEE Conference on Biomedical and Health Informatics 2019, Chicago, IL, USA, 19-22 May, 2019.
Open this publication in new window or tab >>A multimodal sensor fusion platform for objective assessment of motor states in Parkinson's disease
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2019 (English)In: IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI 19), 2019Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This study proposes a platform to objectively assess motor states in Parkinson’s disease (PD) using sensor technology and machine learning. The platform uses sensor information gathered during standardized motor tasks and fuses them in a data-driven manner to produce an index representing motor states of the patients. After investigating clinimetric properties of the platform it was found that the platform had good validity and responsiveness to treatment, which are essential for developing systems to individualize treatments.

National Category
Computer and Information Sciences Information Systems
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-74621 (URN)
Conference
IEEE Conference on Biomedical and Health Informatics 2019, Chicago, IL, USA, 19-22 May, 2019
Funder
Knowledge Foundation
Available from: 2019-06-07 Created: 2019-06-07 Last updated: 2019-06-10Bibliographically approved
Thangavel, G., Memedi, M. & Hedström, K. (2019). A systematic review of Social Internet of Things: concepts and application areas. In: Americas Conference on Information Systems 2019: . Paper presented at 25th Americas Conference on Information Systems (AMCIS 2019), Cancún, Mexico, August 15-17, 2019. Association for Information Systems
Open this publication in new window or tab >>A systematic review of Social Internet of Things: concepts and application areas
2019 (English)In: Americas Conference on Information Systems 2019, Association for Information Systems, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) connects machines, devices, sensors and people. This technology is expected to connect billions of devices in the near future. Traditional methods make it very difficult to integrate and maintain so many devices. However, social networks manage to connect and maintain the communication of billions of people using social relationships. Social IoT (SIoT) is an emerging field that uses social relations to connect and maintain devices in IoT networks. This article presents a systematic literature review of conceptual papers in SIoT, together with application areas. The results show two themes from conceptual papers: Objects part of human social loop and have a role in human social network, and Objects form social network. Furthermore the results indicate that, SIoT is mostly applied in smart home environment. These findings will benefit academics and practitioners to better understand SIoT and its applications areas.

Place, publisher, year, edition, pages
Association for Information Systems, 2019
Keywords
Social Internet of Things, Social Web of Things, Internet of Things, Social Network
National Category
Information Systems
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-75977 (URN)
Conference
25th Americas Conference on Information Systems (AMCIS 2019), Cancún, Mexico, August 15-17, 2019
Available from: 2019-08-29 Created: 2019-08-29 Last updated: 2019-09-02Bibliographically approved
Johansson, D., Thomas, I., Ericsson, A., Johansson, A., Medvedev, A., Memedi, M., . . . Bergquist, F. (2019). Evaluation of a sensor algorithm for motor state rating in Parkinson's disease. Parkinsonism & Related Disorders, 64, 112-117
Open this publication in new window or tab >>Evaluation of a sensor algorithm for motor state rating in Parkinson's disease
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2019 (English)In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126, Vol. 64, p. 112-117Article in journal (Refereed) Published
Abstract [en]

Introduction: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

Methods: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

Results: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (rs = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (rs = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

Conclusion: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Levodopa challenge test, Independent evaluation, Wearable sensors, Parkinson's disease, Machine learning algorithms
National Category
Computer and Information Sciences Neurology
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-73415 (URN)10.1016/j.parkreldis.2019.03.022 (DOI)000487567800016 ()30935826 (PubMedID)2-s2.0-85063430752 (Scopus ID)
Funder
Vinnova, 2014-03727Swedish Foundation for Strategic Research , SBE 13-0086
Note

Funding Agency:

Swedish Government's Regional (ALF) Agreement on Research  ALFGBG-429901

Available from: 2019-03-29 Created: 2019-03-29 Last updated: 2019-11-12Bibliographically approved
Aghanavesi, S., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2019). Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests: results from levodopa challenge. IEEE journal of biomedical and health informatics
Open this publication in new window or tab >>Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests: results from levodopa challenge
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2019 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed) Epub ahead of print
Abstract [en]

Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Keywords
Leg agility, Parkinson's disease, support vector machines, stepwise regression, predictive models
National Category
Computer and Information Sciences Neurology
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-72361 (URN)10.1109/JBHI.2019.2898332 (DOI)
Funder
Knowledge Foundation
Available from: 2019-02-09 Created: 2019-02-09 Last updated: 2019-02-11Bibliographically approved
Thomas, I., Alam, M., Bergquist, F., Johansson, D., Memedi, M., Nyholm, D. & Westin, J. (2019). Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson’s disease: a first experience. Journal of Neurology, 266(3), 651-658
Open this publication in new window or tab >>Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson’s disease: a first experience
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2019 (English)In: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, no 3, p. 651-658Article in journal (Refereed) Published
Abstract [en]

Objective: Dosing schedules for oral levodopa in advanced stages of Parkinson’s disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).

Materials and methods: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson’s KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

Results: The SBDS maintenance and morning dosing suggestions had a Pearson’s correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician’s adjustments.

Conclusion: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2019
Keywords
Levodopa, Parkinson’s disease, Algorithmic suggestions, Sensor data, Oral medication
National Category
Computer and Information Sciences Neurology
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-71616 (URN)10.1007/s00415-019-09183-6 (DOI)000459203400013 ()30659356 (PubMedID)2-s2.0-85060256040 (Scopus ID)
Funder
Knowledge FoundationVinnova
Note

Funding Agencies:

Acreo (Sweden)  

Cenvigo (Sweden)  

Sensidose (Sweden)  

Uppsala University (Sweden)  

Gothenburg University (Sweden)  

Dalarna University (Sweden) 

Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-06-18Bibliographically approved
Karni, L., Memedi, M. & Klein, G. O. (2019). Targeting Patient Empowerment via ICT interventions: An ICT-specific Analytical Framework. In: AMCIS 2019 Proceedings: . Paper presented at 25th Americas Conference on Information Systems (AMCIS 2019), Cancun, Mexico, August 15-17, 2019. Cancun, Mexico: Association for Information Systems
Open this publication in new window or tab >>Targeting Patient Empowerment via ICT interventions: An ICT-specific Analytical Framework
2019 (English)In: AMCIS 2019 Proceedings, Cancun, Mexico: Association for Information Systems, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Empowerment of patients is today often an explicit goal of various ICT interventions where the patients themselves use ICT tools, often via the internet. This study is proposing a framework model for ICT interventions aiming to empower patients. Our new model includes different aspects of the Empowerment concept, general possible strategies to achieve Empowerment using different ICT services. Finally, the ICT services and the underlying strategic model can be used to define evaluations of such interventions where the aim is to demonstrate Empowerment. Our model is based on a review of various general models of Empowerment and the Behavioral Intervention Technology Model (BIT). The implications of our model are discussed using two case studies projects, the C3-Cloud EU project about empowering patients with 4 chronic diseases and the EMPARK project about Internet-of-Things sensors based real time feedback to Parkinson patients.

Place, publisher, year, edition, pages
Cancun, Mexico: Association for Information Systems, 2019
Keywords
Empowerment, ICT-Intervention, Framework mode
National Category
Information Systems
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-75987 (URN)
Conference
25th Americas Conference on Information Systems (AMCIS 2019), Cancun, Mexico, August 15-17, 2019
Projects
EMPARK
Funder
Knowledge Foundation
Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-08-30Bibliographically approved
Thomas, I., Memedi, M., Westin, J. & Nyholm, D. (2019). The effect of continuous levodopa treatment during the afternoon hours. Acta Neurologica Scandinavica, 139(1), 70-75
Open this publication in new window or tab >>The effect of continuous levodopa treatment during the afternoon hours
2019 (English)In: Acta Neurologica Scandinavica, ISSN 0001-6314, E-ISSN 1600-0404, Vol. 139, no 1, p. 70-75Article in journal (Refereed) Published
Abstract [en]

Objective: The aim of this retrospective study was to investigate if patients with PD, who are treated with levodopa‐carbidopa intestinal gel (LCIG), clinically worsen during the afternoon hours and if so, to evaluate whether this occurs in all LCIG‐treated patients or in a sub‐group of patients.

Methods: Three published studies were identified and included in the analysis. All studies provided individual response data assessed on the treatment response scale (TRS) and patients were treated with continuous LCIG. Ninety‐eight patients from the three studies fulfilled the criteria. T‐tests were performed to find differences on the TRS values between the morning and the afternoon hours, linear mixed effect models were fitted on the afternoon hours’ evaluations to find trends of wearing‐off, and patients were classified into three TRS categories (meaningful increase in TRS, meaningful decrease in TRS, non ‐meaningful increase or decrease).

Results: In all three studies significant statistical differences were found between the morning TRS values and the afternoon TRS values (p‐value <= 0.001 in all studies). The linear mixed effect models had significant negative coefficients for time in two studies, and 48 out of 98 patients (49%) showed a meaningful decrease of TRS during the afternoon hours.

Conclusion: The results from all studies were consistent, both in the proportion of patients in the three groups and the value of TRS decrease in the afternoon hours. Based on these findings there seems to be a group of patients with predictable “off” behavior in the later parts of the day.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
diurnal motor fluctuation, infusion pumps, levodopa, Parkinson disease
National Category
Neurology Computer and Information Sciences
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-68788 (URN)10.1111/ane.13020 (DOI)000452067700007 ()30180267 (PubMedID)2-s2.0-85053714059 (Scopus ID)
Note

Funding Agencies:

Dalarna University  

Uppsala University  

Örebro University 

Available from: 2018-09-07 Created: 2018-09-07 Last updated: 2019-01-07Bibliographically approved
Matić, T., Aghnavesi, S., Memedi, M., Nyholm, D., Bergquist, F., Groznik, V., . . . Sadikov, A. (2019). Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease. In: Riaño D., Wilk S., ten Teije A. (Ed.), AIME 2019: Artificial Intelligence in Medicine. Paper presented at 17th Conference on Artificial Intelligence in Medicine (AIME 2019), Poznan, Poland, June 26–29, 2019 (pp. 420-424). Springer, 11526
Open this publication in new window or tab >>Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease
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2019 (English)In: AIME 2019: Artificial Intelligence in Medicine / [ed] Riaño D., Wilk S., ten Teije A., Springer, 2019, Vol. 11526, p. 420-424Conference paper, Published paper (Refereed)
Abstract [en]

Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science ; 11526
Keywords
Unsupervised learning, Motion sensor, Parkinson’s disease, Objective evaluation, Patient monitoring, Bradykinesia, Dyskinesia
National Category
Computer and Information Sciences
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-74749 (URN)10.1007/978-3-030-21642-9_52 (DOI)000495606500052 ()2-s2.0-85068314091 (Scopus ID)978-3-030-21642-9 (ISBN)
Conference
17th Conference on Artificial Intelligence in Medicine (AIME 2019), Poznan, Poland, June 26–29, 2019
Funder
Knowledge FoundationVinnova
Note

Funding Agency:

Slovenian Research Agency (ARRS) under the Artificial Intelligence and Intelligent Systems Programme (ARRS)  P2-0209

Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-11-27Bibliographically approved
Javed, F., Thomas, I. & Memedi, M. (2018). A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease. In: : . Paper presented at 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18), Honolulu, Hawaii, USA, July 17-21, 2018. IEEE
Open this publication in new window or tab >>A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson’s disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Feature extraction, Support vector machines, Principal component analysis, Correlation, Machine learning, Predictive models, Sensors
National Category
Computer and Information Sciences
Research subject
Informatics; Statistics
Identifiers
urn:nbn:se:oru:diva-69954 (URN)10.1109/EMBC.2018.8513683 (DOI)
Conference
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18), Honolulu, Hawaii, USA, July 17-21, 2018
Funder
Knowledge Foundation
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-31Bibliographically approved
Memedi, M., Lindqvist, J., Tunedal, T. & Duvåker, A. (2018). A study on pre-adoption of a self-management application by Parkinson’s disease patients. In: : . Paper presented at 39th International Conference on Information Systems(ICIS 2018), San Francisco, California, USA, December 13-16, 2018. Association for Information Systems
Open this publication in new window or tab >>A study on pre-adoption of a self-management application by Parkinson’s disease patients
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this paper is to provide an overview of factors influencing the acceptance by Parkinson's disease (PD) patients of a self-management application for an Internet of Things system. Unified Theory of Acceptance and Use of Technology (UTAUT) factors including performance expectancy, effort expectancy, and social influence were tested along with sociodemographic (age and gender) and technology-associated (experience with modern technology) factors to determine their contributions for predicting behavioral intention to use the application. Fifty respondents completed the survey. The results show that the UTAUT-based factors, sociodemographic and technology-associated factors account for 82.9% of the variability in PD patients' behavioralintention to use the application. We found that women were significantly more positive than men (p<0.001) in their intention to use the application. If offered the application in the future, 70% of the respondents would use it. Respondents with lower level of experience with technology had less intention to use the application. Performance expectancy and social influence were the only factors that positively predicted intention to use the application. The results showed high scores related to intention to use the application, suggesting high acceptance of the application by the PD patients. Based on qualitative results, the application was seen by PD patients as a useful tool for providing them a better overview of their health status. Finally, the acceptance of the application can be increased by showing its benefits to the PD patients and by developing social strategies to encourage them to stimulate each other to use the application.

Place, publisher, year, edition, pages
Association for Information Systems, 2018
Keywords
Technology acceptance, Internet of Things, patient interface, self-management, Parkinson’s disease, sensor technology
National Category
Human Aspects of ICT
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-70769 (URN)
Conference
39th International Conference on Information Systems(ICIS 2018), San Francisco, California, USA, December 13-16, 2018
Projects
EMPARK
Funder
Knowledge Foundation
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2018-12-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2372-4226

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