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Salvi, D., Ymeri, G., Jimeno, D., Soto-Léon, V., Pérez Borrego, Y., Olsson, C. M. & Carrasco-Lopez, C. (2023). An IoT-based system for the study of neuropathic pain in spinal cord injury. In: Athanasios Tsanas; Andreas Triantafyllidis (Ed.), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceeding. Paper presented at 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022 (pp. 93-103). Springer
Öppna denna publikation i ny flik eller fönster >>An IoT-based system for the study of neuropathic pain in spinal cord injury
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2023 (Engelska)Ingår i: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceeding / [ed] Athanasios Tsanas; Andreas Triantafyllidis, Springer, 2023, s. 93-103Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Neuropathic pain is a difficult condition to treat and would require reliable biomarkers to personalise and optimise treatments. To date, pain levels are mostly measured with subjective scales, but research has shown that electroencephalography (EEG) and heart rate variability (HRV) can be linked to those levels. Internet of Things technology could allow embedding EEG and HRV in easy-to-use systems that patients can use at home in their daily life. We have developed a system for home monitoring that includes a portable EEG device, a tablet application to guide patients through imaginary motor tasks while recording EEG, a wearable HRV sensor and a mobile phone app to report pain levels. We are using this system in a clinical study involving 15 spinal cord injury patients for one month. Preliminary results show that relevant data are being collected, with inter and intra-patients variability for both HRV and pain levels, and that the mobile phone app is perceived as usable, of good quality and useful. However, because of its complexity, the system requires some effort from patients, is sometimes unreliable and the collected EEG signals are not always of the desired quality.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 488
Nyckelord
IoT, EEG, HRV, Neuropathic pain, Mobile health
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-58645 (URN)10.1007/978-3-031-34586-9_7 (DOI)2-s2.0-85164160734 (Scopus ID)978-3-031-34585-2 (ISBN)978-3-031-34586-9 (ISBN)
Konferens
16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022
Forskningsfinansiär
EU, Horisont Europa, 101030384
Tillgänglig från: 2023-03-14 Skapad: 2023-03-14 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Tsang, K. C., Pinnock, H., Wilson, A. M., Salvi, D., Olsson, C. M. & Syed Ahmar, S. (2023). Compliance and Usability of an Asthma Home Monitoring System. In: Athanasios Tsanas; Andreas Triantafyllidis (Ed.), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings. Paper presented at 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022 (pp. 116-126). Springer
Öppna denna publikation i ny flik eller fönster >>Compliance and Usability of an Asthma Home Monitoring System
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2023 (Engelska)Ingår i: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings / [ed] Athanasios Tsanas; Andreas Triantafyllidis, Springer, 2023, s. 116-126Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Asthma monitoring is an important aspect of patient self-management. However, due to its repetitive nature, patients can find long-term monitoring tedious. Mobile health can provide an avenue to monitor asthma without needing high levels of active engagement, and instead rely on passive monitoring. In our recent AAMOS-00 study, we collected mobile health data over six months from 22 asthma patients using passive and active monitoring technology, including smartwatch, peak flow measurements, and daily asthma diaries.

Compliance to smartwatch monitoring was found to lie between the compliance to complete daily asthma diaries and measuring daily peak flow. However, some study participants faced technical issues with the devices which could have affected the relative compliance of the monitoring tasks.

Moreover, as evidenced by standard usability questionnaires, we found that the AAMOS-00 study’s data collection system was similar in quality to other studies and published apps.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 488
Nyckelord
Asthma, Mobile Health, mHealth, Home Monitoring, Compliance, Passive Monitoring
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-58644 (URN)10.1007/978-3-031-34586-9_9 (DOI)2-s2.0-85164103209 (Scopus ID)978-3-031-34585-2 (ISBN)978-3-031-34586-9 (ISBN)
Konferens
16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022
Tillgänglig från: 2023-03-14 Skapad: 2023-03-14 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Ymeri, G., Salvi, D., Olsson, C. M., Wassenburg, M. V., Tsanas, A. & Svenningsson, P. (2023). Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol. BMJ Open, 13(12), Article ID e077766.
Öppna denna publikation i ny flik eller fönster >>Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
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2023 (Engelska)Ingår i: BMJ Open, E-ISSN 2044-6055, Vol. 13, nr 12, artikel-id e077766Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

INTRODUCTION: The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

AIM: To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

METHODS AND ANALYSIS: In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

ETHICS AND DISSEMINATION: The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

Ort, förlag, år, upplaga, sidor
BMJ Publishing Group Ltd, 2023
Nyckelord
health informatics, parkinson's disease, telemedicine
Nationell ämneskategori
Neurologi
Identifikatorer
urn:nbn:se:mau:diva-64860 (URN)10.1136/bmjopen-2023-077766 (DOI)001134943800008 ()38154904 (PubMedID)2-s2.0-85181165016 (Scopus ID)
Tillgänglig från: 2024-01-08 Skapad: 2024-01-08 Senast uppdaterad: 2024-03-07Bibliografiskt granskad
Engström, J., Jevinger, Å., Olsson, C. M. & Persson, J. A. (2023). Some Design Considerations in Passive Indoor Positioning Systems. Sensors, 23(12), Article ID 5684.
Öppna denna publikation i ny flik eller fönster >>Some Design Considerations in Passive Indoor Positioning Systems
2023 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 12, artikel-id 5684Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Ort, förlag, år, upplaga, sidor
MDPI, 2023
Nyckelord
BLE, fingerprinting, indoor positioning, multilateration, RSSI, privacy
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:mau:diva-61951 (URN)10.3390/s23125684 (DOI)001017806900001 ()37420850 (PubMedID)2-s2.0-85163999180 (Scopus ID)
Tillgänglig från: 2023-08-17 Skapad: 2023-08-17 Senast uppdaterad: 2023-10-03Bibliografiskt granskad
Ymeri, G., Salvi, D. & Olsson, C. M. (2022). Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study. In: Tsanas, Athanasios; Triantafyllidis, Andreas (Ed.), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings. Paper presented at 16th EAI International Conference, Pervasive Health 2022, Thessaloniki, Greece, December 12-14, 2022. Cham: Springer
Öppna denna publikation i ny flik eller fönster >>Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study
2022 (Engelska)Ingår i: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings / [ed] Tsanas, Athanasios; Triantafyllidis, Andreas, Cham: Springer, 2022Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

Ort, förlag, år, upplaga, sidor
Cham: Springer, 2022
Serie
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211
Nyckelord
mobile health, Parkinson’s disease, mPower data
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-58646 (URN)10.1007/978-3-031-34586-9_29 (DOI)2-s2.0-85164108273 (Scopus ID)978-3-031-34585-2 (ISBN)978-3-031-34586-9 (ISBN)
Konferens
16th EAI International Conference, Pervasive Health 2022, Thessaloniki, Greece, December 12-14, 2022
Tillgänglig från: 2023-03-14 Skapad: 2023-03-14 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Ymeri, G., Salvi, D., Olsson, C. M., Thanasis, T. & Svenningsson, P. (2022). Mobile-based multi-dimensional data collection for Parkinson’s symptoms in home environments. In: : . Paper presented at 44th International Engineering in Medicine and Biology, 11-15 July 2022, Glasgow, UK.
Öppna denna publikation i ny flik eller fönster >>Mobile-based multi-dimensional data collection for Parkinson’s symptoms in home environments
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2022 (Engelska)Konferensbidrag, Poster (med eller utan abstract) (Refereegranskat)
Abstract [en]

We extended the Mobistudy app for clinical research in order to gather data about Parkinson’s motor and non-motor symptoms. We developed 5 tests that make use of the phone’s embedded sensors and 3 questionnaires. We show through data collected by healthy individuals simulating PD symptoms that the tests are able to identify the presence of symptoms.

Nyckelord
mobile health, Parkinson’s disease
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:mau:diva-59125 (URN)
Konferens
44th International Engineering in Medicine and Biology, 11-15 July 2022, Glasgow, UK
Tillgänglig från: 2023-04-05 Skapad: 2023-04-05 Senast uppdaterad: 2024-01-08Bibliografiskt granskad
Salvi, D., Olsson, C. M., Ymeri, G., Carrasco-Lopez, C., Tsang, K. C. .. & Shah, S. A. (2022). Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies. In: IoT 2021: Conference Proceedings. Paper presented at 11th International Conference on the Internet of Things, November 8-11, 2021. St.Gallen, Switzerland (pp. 219-222). ACM Digital Library
Öppna denna publikation i ny flik eller fönster >>Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies
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2022 (Engelska)Ingår i: IoT 2021: Conference Proceedings, ACM Digital Library, 2022, s. 219-222Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Internet of Things (IoT) can work as a useful tool for clinical research. We developed a software platform that allows researchers to publish clinical studies and volunteers to participate into them using an app and connected IoT devices. The platform includes a REST API, a web interface for researchers and an app that collects data during tasks volunteers are invited to contribute. Nine tasks have been developed: Forms, Positioning, Finger tapping, Pulse-oximetry, Peak Flow measurement, Activity tracking, Data query, Queen’s College step test and Six-minute walk test. These leverage sensors embedded in the phone, connected Bluetooth devices and additional APIs like HealthKit and Google Fit. Currently, the platform is used in two clinical studies by 25 patients: an asthma management study in the United Kingdom, and a neuropathic pain management study in Spain.

Ort, förlag, år, upplaga, sidor
ACM Digital Library, 2022
Nyckelord
clinical research, m-Health, IoT
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-50618 (URN)10.1145/3494322.3494363 (DOI)000936000600025 ()2-s2.0-85127119368 (Scopus ID)978-1-4503-8566-4 (ISBN)
Konferens
11th International Conference on the Internet of Things, November 8-11, 2021. St.Gallen, Switzerland
Forskningsfinansiär
KK-stiftelsen, 20140035
Tillgänglig från: 2022-03-14 Skapad: 2022-03-14 Senast uppdaterad: 2024-01-08Bibliografiskt granskad
Alawadi, S., Kebande, V. R., Dong, Y., Bugeja, J., Persson, J. A. & Olsson, C. M. (2021). A Federated Interactive Learning IoT-Based Health Monitoring Platform. In: New Trends in Database and Information Systems: . Paper presented at ADBIS 2021: New Trends in Database and Information Systems. Tartu, Estonia, August 24-26, 2021. (pp. 235-246). Springer
Öppna denna publikation i ny flik eller fönster >>A Federated Interactive Learning IoT-Based Health Monitoring Platform
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2021 (Engelska)Ingår i: New Trends in Database and Information Systems, Springer, 2021, s. 235-246Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as ‘Human-in-the-loop’ in an active learning strategy in order to improve the clients’ Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model’s accuracy is improved while privacy and security of data has been upheld.

Ort, förlag, år, upplaga, sidor
Springer, 2021
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1450
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:mau:diva-47470 (URN)10.1007/978-3-030-85082-1_21 (DOI)000775759800021 ()2-s2.0-85115134304 (Scopus ID)978-3-030-85081-4 (ISBN)978-3-030-85082-1 (ISBN)
Konferens
ADBIS 2021: New Trends in Database and Information Systems. Tartu, Estonia, August 24-26, 2021.
Tillgänglig från: 2021-12-13 Skapad: 2021-12-13 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Jevinger, Å. & Olsson, C. M. (2021). Introducing an Intelligent Goods Service Framework. Logistics, 5(3), Article ID 54.
Öppna denna publikation i ny flik eller fönster >>Introducing an Intelligent Goods Service Framework
2021 (Engelska)Ingår i: Logistics, ISSN 2305-6290, Vol. 5, nr 3, artikel-id 54Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

With the increasing diffusion of Internet of Things (IoT) technologies, the transportation of goods sector is in a position to adopt novel intelligent services that cut across the otherwise highly fragmented and heterogeneous market, which today consists of a myriad of actors. Legacy systems that rely upon direct integration between all actors involved in the transportation ecosystem face considerable challenges for information sharing. Meanwhile, IoT based services, which are designed as devices that follow goods and communicate directly to cloud-based backend systems, may provide services that previously were not available. For the purposes of this paper, we present a theoretical framework for classification of such intelligent goods systems based on a literature study. The framework, labelled as the Intelligent Goods Service (IGS) framework, aims at increasing the understanding of the actors, agents, and services involved in an intelligent goods system, and to facilitate system comparisons and the development of new innovative solutions. As an illustration of how the IGS framework can be used and contribute to research in this area, we provide an example from a direct industry-academia collaboration.

Ort, förlag, år, upplaga, sidor
MDPI, 2021
Nyckelord
intelligent goods services, Internet of Things, system classification, theoretical framework
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mau:diva-46252 (URN)10.3390/logistics5030054 (DOI)000702839900001 ()2-s2.0-85124940271 (Scopus ID)
Tillgänglig från: 2021-10-12 Skapad: 2021-10-12 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Maus, B., Olsson, C. M. & Salvi, D. (2021). Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach. Frontiers in Digital Health, 3, 1-12, Article ID 675754.
Öppna denna publikation i ny flik eller fönster >>Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach
2021 (Engelska)Ingår i: Frontiers in Digital Health, E-ISSN 2673-253X, Vol. 3, s. 1-12, artikel-id 675754Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.

Ort, förlag, år, upplaga, sidor
Frontiers Media S.A., 2021
Nyckelord
Citizen science, IoT-based health research, privacy calculus, privacy personas, survey
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-48206 (URN)10.3389/fdgth.2021.675754 (DOI)001033156000001 ()34977856 (PubMedID)2-s2.0-85131240250 (Scopus ID)
Tillgänglig från: 2021-12-16 Skapad: 2021-12-16 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Projekt
Forskningsprofilen Internet of Things and People; Malmö universitet; Publikationer
Banda, L., Mjumo, M. & Mekuria, F. (2022). Business Models for 5G and Future Mobile Network Operators. In: 2022 IEEE Future Networks World Forum (FNWF): . Paper presented at IEEE Future Networks World Forum FNWF 2022, Montreal, QC, Canada, 10-14 October 2022. IEEE, Article ID M17754.
The Evolutionary World Designer; Malmö universitetContext-Aware and Autonomous Behavior: Making sense of IoTAVANS projekt: "Internet of Things Master's Program"; Malmö universitetmHälsa vid pandemier: Smartphone-baserade portabla och bärbara sensorer för COVID-19 diagnostik; Malmö universitetParkappPain App: Förutsäger neuropatiska smärtepisoder hos patienter med ryggmärgsskada genom bärbar EEG och maskininlärning; Malmö universitet
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-4261-281X

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