Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach
Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labele...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/366 |
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| author | Luc J. W. Evers Yordan P. Raykov Tom M. Heskes Jesse H. Krijthe Bastiaan R. Bloem Max A. Little |
| author_facet | Luc J. W. Evers Yordan P. Raykov Tom M. Heskes Jesse H. Krijthe Bastiaan R. Bloem Max A. Little |
| author_sort | Luc J. W. Evers |
| collection | DOAJ |
| description | Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls. We used wrist accelerometer data and synchronous expert video annotations for the presence of tremor, captured during unscripted daily life activities in and around the participants’ own homes. Based on leave-one-subject-out cross-validation, we demonstrate the ability of prototypical networks to capture free-living tremor episodes. Specifically, we demonstrate that prototypical networks can be used to enforce robust performance across domain-informed sub-classes, including different tremor phenotypes and daily life activities. |
| format | Article |
| id | doaj-art-4e5205d55fc947338f20f9929591c136 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4e5205d55fc947338f20f9929591c1362025-01-24T13:48:39ZengMDPI AGSensors1424-82202025-01-0125236610.3390/s25020366Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network ApproachLuc J. W. Evers0Yordan P. Raykov1Tom M. Heskes2Jesse H. Krijthe3Bastiaan R. Bloem4Max A. Little5Center of Expertise for Parkinson and Movement Disorders, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsSchool of Mathematical Sciences, University of Nottingham, Nottingham NG9 2SE, UKInstitute for Computing and Information Sciences, Radboud University, 6525 EC Nijmegen, The NetherlandsPattern Recognition & Bioinformatics Group, Delft University of Technology, 2628 XE Delft, The NetherlandsCenter of Expertise for Parkinson and Movement Disorders, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsSchool of Computer Science, University of Birmingham, Birmingham B15 2TT, UKObjective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls. We used wrist accelerometer data and synchronous expert video annotations for the presence of tremor, captured during unscripted daily life activities in and around the participants’ own homes. Based on leave-one-subject-out cross-validation, we demonstrate the ability of prototypical networks to capture free-living tremor episodes. Specifically, we demonstrate that prototypical networks can be used to enforce robust performance across domain-informed sub-classes, including different tremor phenotypes and daily life activities.https://www.mdpi.com/1424-8220/25/2/366tremor modelingtremor detectionpassive monitoringwearable sensorsprototype networksParkinson’s disease |
| spellingShingle | Luc J. W. Evers Yordan P. Raykov Tom M. Heskes Jesse H. Krijthe Bastiaan R. Bloem Max A. Little Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach Sensors tremor modeling tremor detection passive monitoring wearable sensors prototype networks Parkinson’s disease |
| title | Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach |
| title_full | Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach |
| title_fullStr | Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach |
| title_full_unstemmed | Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach |
| title_short | Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach |
| title_sort | passive monitoring of parkinson tremor in daily life a prototypical network approach |
| topic | tremor modeling tremor detection passive monitoring wearable sensors prototype networks Parkinson’s disease |
| url | https://www.mdpi.com/1424-8220/25/2/366 |
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