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Caramaschi, S., Ymeri, G., Olsson, C. M., Tsanas, A., Wassenburg, M., Svenningsson, P. & Salvi, D. (2024). A Smartphone-Based Timed Up and Go Test for Parkinson’s Disease. In: Dario Salvi, Pieter Van Gorp, Syed Ahmar Shah (Ed.), Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings. Paper presented at 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023 (pp. 515-519). Springer
Open this publication in new window or tab >>A Smartphone-Based Timed Up and Go Test for Parkinson’s Disease
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2024 (English)In: Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings / [ed] Dario Salvi, Pieter Van Gorp, Syed Ahmar Shah, Springer, 2024, p. 515-519Conference paper, Published paper (Refereed)
Abstract [en]

The Timed-Up and Go test is a simple yet effective test used to evaluate balance and mobility in conditions that affect movement, such as Parkinson’s disease. This test can inform clinicians about the monitoring and progression of the disease by measuring the time taken to complete the test. We used a smartphone app to obtain the phone’s inertial data and implemented an algorithm to automatically extract the time taken to complete the test. We considered data collected from six healthy participants performing tests at different speeds. The proposed method was further tested on twelve participants with Parkinson’s disease based on a reference measurement in clinic. We show that, for both groups, we obtain good accuracy (RMSE = 3.42 and 1.95 s) and a strong positive correlation (r = 0.85 and 0.83) between estimated duration and ground truth. We highlight limitations in our approach when the test is performed at very low speed or without a clear pause between the test and the user interaction with the phone. 

Place, publisher, year, edition, pages
Springer, 2024
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 572
National Category
Medical Engineering
Identifiers
urn:nbn:se:mau:diva-70310 (URN)10.1007/978-3-031-59717-6_34 (DOI)2-s2.0-85196783281 (Scopus ID)978-3-031-59716-9 (ISBN)978-3-031-59717-6 (ISBN)
Conference
17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023
Available from: 2024-08-16 Created: 2024-08-16 Last updated: 2024-08-16Bibliographically approved
Caramaschi, S., Bezançon, J., Olsson, C. M. & Salvi, D. (2024). An IoT-Based Method for Collecting Reference Walked Distance for the 6-Minute Walk Test. In: Dario Salvi, Pieter Van Gorp, Syed Ahmar Shah (Ed.), Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings. Paper presented at 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023 (pp. 478-489). Springer
Open this publication in new window or tab >>An IoT-Based Method for Collecting Reference Walked Distance for the 6-Minute Walk Test
2024 (English)In: Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings / [ed] Dario Salvi, Pieter Van Gorp, Syed Ahmar Shah, Springer, 2024, p. 478-489Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the need for accurate and continuous measurement of walked distance in applications such as indoor localisation, gait analysis or the 6-minute walk test (6MWT). We propose a method to continuously collect ground truth data of walked distance using an IoT-based trundle wheel. The wheel is connected via Bluetooth Low Energy to a smartphone application which allows the collection of inertial sensor data and GPS location information in addition to the reference distance. We prove the usefulness of this data collection approach in a use case where we derive walked distance from inertial data. We train a 1-dimensional CNN on inertial data collected by one researcher in 15 walking sessions of 1 km length at varying speeds. The training is facilitated by the continuous nature of the reference data. The accuracy of the algorithm is then tested on holdout data of a 6-min duration for which the error of the inferred distance is within clinically significant limits. The proposed approach is useful for the efficient collection of input and reference data for the development of algorithms used to estimate walked distance, such as for the 6MWT. 

Place, publisher, year, edition, pages
Springer, 2024
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 572
National Category
Civil Engineering
Identifiers
urn:nbn:se:mau:diva-70308 (URN)10.1007/978-3-031-59717-6_31 (DOI)2-s2.0-85196854728 (Scopus ID)978-3-031-59716-9 (ISBN)978-3-031-59717-6 (ISBN)
Conference
17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023
Available from: 2024-08-16 Created: 2024-08-16 Last updated: 2024-08-16Bibliographically approved
Caramaschi, S., Olsson, C. M., Orchard, E., Molloy, J. & Salvi, D. (2024). Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT. Sensors, 24(8), Article ID 2632.
Open this publication in new window or tab >>Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT
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2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 8, article id 2632Article in journal (Refereed) Published
Abstract [en]

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
6MWT, distance estimation, data reliability, physical assessment
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mau:diva-67314 (URN)10.3390/s24082632 (DOI)001210676000001 ()38676249 (PubMedID)2-s2.0-85191480367 (Scopus ID)
Available from: 2024-05-20 Created: 2024-05-20 Last updated: 2024-05-20Bibliographically approved
Ymeri, G., Grech, N. S., Wassenburg, M., Olsson, C. M., Svenningsson, P. & Salvi, D. (2024). Measuring finger dexterity in Parkinson's disease with mobile phones. In: 2024 IEEE International Conference on Pervasive Computing and Communications: workshops and other affiliated events, percom workshops. Paper presented at IEEE International Conference on Pervasive Computing and Communications (PerCom), MAR 11-15, 2024, Biarritz, FRANCE (pp. 112-116). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Measuring finger dexterity in Parkinson's disease with mobile phones
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2024 (English)In: 2024 IEEE International Conference on Pervasive Computing and Communications: workshops and other affiliated events, percom workshops, Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 112-116Conference paper, Published paper (Refereed)
Abstract [en]

This work aims to link finger tapping and drawing tests performed on mobile phone screens with clinical ratings of Parkinson's Disease (PD). Thirty PD patients were recruited and instructed to carry out these tests in their homes. Features were extracted and used to assess the validity of the data vis a vis clinical scales (MDS-UPDRS). Statistical tests show that several features correlate with clinical scores (max correlation 0.54) and significant differences between data collected before and after medication intake (p<0.05), demonstrating the clinical validity of smartphone data. The use of Machine Learning (ML) algorithms to regress Part-3 of MDS-UPDRS further supports the validity with an absolute mean error of 6.25 (over a 0-72 scale).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
IEEE Annual Conference on Pervasive Computing and Communications Workshops, ISSN 2836-5348
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:mau:diva-69978 (URN)10.1109/PerComWorkshops59983.2024.10503245 (DOI)001216220000036 ()2-s2.0-85192479973 (Scopus ID)979-8-3503-0436-7 (ISBN)979-8-3503-0437-4 (ISBN)
Conference
IEEE International Conference on Pervasive Computing and Communications (PerCom), MAR 11-15, 2024, Biarritz, FRANCE
Available from: 2024-07-30 Created: 2024-07-30 Last updated: 2024-10-30Bibliographically approved
Salvi, D., Van Gorp, P. & Shah, S. A. (Eds.). (2024). Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023. Springer
Open this publication in new window or tab >>Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023
2024 (English)Collection (editor) (Refereed)
Abstract [en]

This book constitutes the refereed proceedings of the 17th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2023, held in Malmö, Sweden, during November 27-29, 2023.The 29 full papers and 6 short papers were selected from 90 submissions and are organized in thematic sessions as follows: Pervasive Mental Health; Privacy, Ethics and Regulations; Datasets and Big data Processing; Pervasive health for Carers; Pervasive Health in Clinical Practice; Remote Monitoring; Patient and User Aspects; Motion and rehabilitation; Workshop on the Internet of Things in Health Research; Posters and demos (non indexed annex).

Place, publisher, year, edition, pages
Springer, 2024. p. 527
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 572
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-70309 (URN)10.1007/978-3-031-59717-6 (DOI)978-3-031-59716-9 (ISBN)978-3-031-59717-6 (ISBN)
Available from: 2024-08-16 Created: 2024-08-16 Last updated: 2024-08-16Bibliographically approved
Strange, M., Mangrio, E., Olsson, C. M., Salvi, D., Bagheri, S. & Maus, B. (2024). Utgå inte från att AI alltid är lösningen i vården: Innovation kring hur vi använder AI i vården får inte bara bero på privata företag, skriver forskare från Malmö universitet som vill ta reda på vad som behövs för att bygga pålitlig AI. Dagens Samhälle (2024-10-24)
Open this publication in new window or tab >>Utgå inte från att AI alltid är lösningen i vården: Innovation kring hur vi använder AI i vården får inte bara bero på privata företag, skriver forskare från Malmö universitet som vill ta reda på vad som behövs för att bygga pålitlig AI
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2024 (Swedish)In: Dagens Samhälle, ISSN 1652-6511, no 2024-10-24Article in journal, News item (Other (popular science, discussion, etc.)) Published
Place, publisher, year, edition, pages
Bonnier Business Media AB, 2024
National Category
Public Health, Global Health, Social Medicine and Epidemiology Globalisation Studies Computer Systems
Research subject
Health and society; Global politics; Interaktionsdesign
Identifiers
urn:nbn:se:mau:diva-71798 (URN)
Projects
Multistakeholder perspectives and experience of trust in digital health and AI
Available from: 2024-10-25 Created: 2024-10-25 Last updated: 2024-10-28Bibliographically approved
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
Open this publication in new window or tab >>An IoT-based system for the study of neuropathic pain in spinal cord injury
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2023 (English)In: 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, p. 93-103Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 488
Keywords
IoT, EEG, HRV, Neuropathic pain, Mobile health
National Category
Computer Sciences
Identifiers
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)
Conference
16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022
Funder
EU, Horizon Europe, 101030384
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2024-06-11Bibliographically approved
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
Open this publication in new window or tab >>Compliance and Usability of an Asthma Home Monitoring System
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2023 (English)In: 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, p. 116-126Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 488
Keywords
Asthma, Mobile Health, mHealth, Home Monitoring, Compliance, Passive Monitoring
National Category
Computer Sciences
Identifiers
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)
Conference
16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2024-06-11Bibliographically approved
Tsang, K. C., Pinnock, H., Wilson, A. M., Salvi, D. & Shah, S. A. (2023). Home monitoring with connected mobile devices for asthma attack prediction with machine learning. Scientific Data, 10(1), Article ID 370.
Open this publication in new window or tab >>Home monitoring with connected mobile devices for asthma attack prediction with machine learning
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2023 (English)In: Scientific Data, E-ISSN 2052-4463, Vol. 10, no 1, article id 370Article in journal (Refereed) Published
Abstract [en]

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Place, publisher, year, edition, pages
Nature Publishing Group, 2023
National Category
Respiratory Medicine and Allergy
Identifiers
urn:nbn:se:mau:diva-61395 (URN)10.1038/s41597-023-02241-9 (DOI)001003519300002 ()37291158 (PubMedID)2-s2.0-85161336943 (Scopus ID)
Available from: 2023-06-27 Created: 2023-06-27 Last updated: 2024-05-20Bibliographically approved
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.
Open this publication in new window or tab >>Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
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2023 (English)In: BMJ Open, E-ISSN 2044-6055, Vol. 13, no 12, article id e077766Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2023
Keywords
health informatics, parkinson's disease, telemedicine
National Category
Neurology
Identifiers
urn:nbn:se:mau:diva-64860 (URN)10.1136/bmjopen-2023-077766 (DOI)001134943800008 ()38154904 (PubMedID)2-s2.0-85181165016 (Scopus ID)
Available from: 2024-01-08 Created: 2024-01-08 Last updated: 2024-10-30Bibliographically approved
Projects
Context-Aware and Autonomous Behavior: Making sense of IoTmHealth in pandemic situations: Smartphone based portable and wearable sensors for COVID-19 diagnostic; Malmö UniversityParkappPain App: Predicting neuropathic pain episodes in spinal cord injury patients through portable EEG and machine learning; Malmö University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9203-1124

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