Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors.
16 affiliations
- 1VTT Technical Research Centre of Finland Ltd, Oulu, Finland.
- 2Future Healthcare Services Expertise Group, Northern Well-being and services, Lapland University of Applied Sciences, Rovaniemi, Finland.
- 3VTT Technical Research Centre of Finland Ltd, Tampere, Finland.
- 4VTT Technical Research Centre of Finland Ltd, Espoo, Finland.
- 5Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- 6Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, North Ostrobothnia, Finland.
- 7Oulu University Hospital, Oulu, Finland.
- 8Health and Biomedical Sciences, Royal Melbourne Institute of Technology, Melbourne, Victoria, Australia.
- 9Martti Ahtisaari Institute, Oulu Business School, University of Oulu, Oulu, North Ostrobothnia, Finland.
- 10Faculty of Health Sciences and Faculty of Technology, Art and Design, OsloMet - Oslo Metropolitan University, Oslo, Norway.
- 11Optomed Plc, Oulu, Finland.
- 12Research Unit of Clinical Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland.
- 13Computer Science and Engineering, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, North Ostrobothnia, Finland.
- 14Medical Research Center Oulu, Oulu, Finland.
- 15Kuopio University Hospital, Kuopio, Finland.
- 16Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.
Abstract
Background
Stroke is the second leading cause of death worldwide. Cerebrovascular diseases (CVDs), including strokes and transient ischemic attacks (TIAs), cause long-term disabilities and economic burdens. Strokes can be ischemic, caused by blood clots; or hemorrhagic, caused by bleeding in the brain. Immediate diagnosis and treatment are crucial. Proper management of TIAs is essential due to the high risk of subsequent strokes. Currently, wearable sensors, AI-based prediction, and automatic video analysis are not used in CVD diagnosis and recovery estimation.Objective
This paper describes the design, cohort characteristics, and multimodal dataset of the Stroke-Data study conducted at 2 university hospitals in Finland. Although findings from individual components of the study have been reported elsewhere, this paper provides the first comprehensive description of the overall study design, cohort, and multimodal data collection protocol. The study collected a unique multimodal dataset comprising sensor data, video recordings, clinical assessments, questionnaires, and health record data from healthy controls and patients with stroke and TIA to support the development of AI, sensor-based, and video-based methods for CVD diagnosis and recovery estimation.Methods
Stroke-Data was a prospective national multicenter study conducted at Oulu University Hospital and Kuopio University Hospital from October 2021 to December 2022. A rich multimodal dataset from patients with stroke and TIA and healthy controls was collected by study nurses and a research assistant. Inclusion criteria for patients were age >18 years and a diagnosis of cerebrovascular accident or TIA within the previous 3 days. Healthy control participants were aged 18 years and older and had no major chronic diseases, although blood pressure and cholesterol medications were permitted. Data collection included electroencephalogram; electrocardiogram; near-infrared spectroscopy; accelerometers to analyze gait and balance; retinal fundus images; video recordings of neurological examinations to analyze the face, body movements, and speech; clinical assessments; questionnaires; and electronic health records.Results
In total, 263 participants were recruited, of whom 123 were controls (median age 59, IQR 45-72 years; female: 84/123, 68.3%), 31 were patients with TIA (median age 71, IQR 60-80 years; female: 8/31, 25.8%), and 103 were patients with stroke (median age 70, IQR 59-76 years; female: 42/102, 41.2%, sex data missing from 1 participant). A total of 6 participants were excluded (5 had diagnoses other than TIA or stroke and 1 control participant had a previous CVD).Conclusions
We successfully conducted a multicenter multimodal data collection study on healthy controls and patients with stroke and TIA. This protocol paper provides a comprehensive methodological description of the Stroke-data study, serving as a reference for previously published and future analyses based on the dataset. The protocol is unique in combining physiological sensor data, video recordings, clinical assessments, questionnaires, and health record data from patients with stroke and TIA and healthy controls in a multicenter setting.International registered report identifier (irrid)
DERR1-10.2196/86930.
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BibTeX
@article{immonen2026novel,
title = {{Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors.}},
author = {Milla Immonen and Hilkka Liedes and Aysen Degerli and Ilona Ruotsalainen and Juha Pajula and Mika Hilvo and Heidi Similä and Adil Umer and Mark van Gils and Miia Jansson and Kirsi Maaria Rasmus and Minna Pikkarainen and Petri Huhtinen and Mikko Kärppä and Julius Francis Gomes and Hany Ferdinando and Miguel Bordallo López and Teemu Myllylä and Vesa Kiviniemi and Mikael von Und Zu Fraunberg and Pekka Jäkälä},
journal = {JMIR research protocols},
year = {2026},
month = oct,
doi = {10.2196/86930},
url = {https://europepmc.org/article/MED/42849037},
note = {CC BY},
}
RIS
TY - JOUR
TI - Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors.
AU - Milla Immonen
AU - Hilkka Liedes
AU - Aysen Degerli
AU - Ilona Ruotsalainen
AU - Juha Pajula
AU - Mika Hilvo
AU - Heidi Similä
AU - Adil Umer
AU - Mark van Gils
AU - Miia Jansson
AU - Kirsi Maaria Rasmus
AU - Minna Pikkarainen
AU - Petri Huhtinen
AU - Mikko Kärppä
AU - Julius Francis Gomes
AU - Hany Ferdinando
AU - Miguel Bordallo López
AU - Teemu Myllylä
AU - Vesa Kiviniemi
AU - Mikael von Und Zu Fraunberg
AU - Pekka Jäkälä
PY - 2026
DA - 2026/10/08
JO - JMIR research protocols
DO - 10.2196/86930
UR - https://europepmc.org/article/MED/42849037
AB - Background Stroke is the second leading cause of death worldwide. Cerebrovascular diseases (CVDs), including strokes and transient ischemic attacks (TIAs), cause long-term disabilities and economic burdens. Strokes can be ischemic, caused by blood clots; or hemorrhagic, caused by bleeding in the brain. Immediate diagnosis and treatment are crucial. Proper management of TIAs is essential due to the high risk of subsequent strokes. Currently, wearable sensors, AI-based prediction, and automatic video analysis are not used in CVD diagnosis and recovery estimation.Objective This paper describes the design, cohort characteristics, and multimodal dataset of the Stroke-Data study conducted at 2 university hospitals in Finland. Although findings from individual components of the study have been reported elsewhere, this paper provides the first comprehensive description of the overall study design, cohort, and multimodal data collection protocol. The study collected a unique multimodal dataset comprising sensor data, video recordings, clinical assessments, questionnaires, and health record data from healthy controls and patients with stroke and TIA to support the development of AI, sensor-based, and video-based methods for CVD diagnosis and recovery estimation.Methods Stroke-Data was a prospective national multicenter study conducted at Oulu University Hospital and Kuopio University Hospital from October 2021 to December 2022. A rich multimodal dataset from patients with stroke and TIA and healthy controls was collected by study nurses and a research assistant. Inclusion criteria for patients were age >18 years and a diagnosis of cerebrovascular accident or TIA within the previous 3 days. Healthy control participants were aged 18 years and older and had no major chronic diseases, although blood pressure and cholesterol medications were permitted. Data collection included electroencephalogram; electrocardiogram; near-infrared spectroscopy; accelerometers to analyze gait and balance; retinal fundus images; video recordings of neurological examinations to analyze the face, body movements, and speech; clinical assessments; questionnaires; and electronic health records.Results In total, 263 participants were recruited, of whom 123 were controls (median age 59, IQR 45-72 years; female: 84/123, 68.3%), 31 were patients with TIA (median age 71, IQR 60-80 years; female: 8/31, 25.8%), and 103 were patients with stroke (median age 70, IQR 59-76 years; female: 42/102, 41.2%, sex data missing from 1 participant). A total of 6 participants were excluded (5 had diagnoses other than TIA or stroke and 1 control participant had a previous CVD).Conclusions We successfully conducted a multicenter multimodal data collection study on healthy controls and patients with stroke and TIA. This protocol paper provides a comprehensive methodological description of the Stroke-data study, serving as a reference for previously published and future analyses based on the dataset. The protocol is unique in combining physiological sensor data, video recordings, clinical assessments, questionnaires, and health record data from patients with stroke and TIA and healthy controls in a multicenter setting.International registered report identifier (irrid) DERR1-10.2196/86930.
SN - 1929-0748
ER -