Academic Journal
Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones
| Τίτλος: | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
|---|---|
| Συγγραφείς: | Larsen, Aske G, Sadolin, Line Ø, Thomsen, Trine R, Oliveira, Anderson S |
| Πηγή: | Larsen, A G, Sadolin, L Ø, Thomsen, T R & Oliveira, A S 2025, 'Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones', Sensors (Basel, Switzerland), vol. 25, no. 14, 4470. https://doi.org/10.3390/s25144470 |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | Aalborg University (AAU): Publications / Aalborg Universitet: Publikationer |
| Θεματικοί όροι: | Adult, Digital health, Female, Gait Analysis/methods, Gait analysis, Gait/physiology, Humans, IMU, Machine Learning, Male, Neural Networks, Computer, Remote monitoring, Smartphone, Walking/physiology |
| Περιγραφή: | As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| ISSN: | 1424-8220 1424-8239 |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/40732598; info:eu-repo/semantics/altIdentifier/pissn/1424-8220; info:eu-repo/semantics/altIdentifier/eissn/1424-8239 |
| DOI: | 10.3390/s25144470 |
| Διαθεσιμότητα: | https://vbn.aau.dk/da/publications/6903ab62-ca01-443d-bad2-48f0b7cd1c05 https://doi.org/10.3390/s25144470 https://vbn.aau.dk/ws/files/790776504/Open_Access_Article.pdf https://www.scopus.com/pages/publications/105011535555 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Αριθμός Καταχώρησης: | edsbas.611B7C11 |
| Βάση Δεδομένων: | BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Larsen%2C+Aske+G%22">Larsen, Aske G</searchLink><br /><searchLink fieldCode="AR" term="%22Sadolin%2C+Line+Ø%22">Sadolin, Line Ø</searchLink><br /><searchLink fieldCode="AR" term="%22Thomsen%2C+Trine+R%22">Thomsen, Trine R</searchLink><br /><searchLink fieldCode="AR" term="%22Oliveira%2C+Anderson+S%22">Oliveira, Anderson S</searchLink> – Name: TitleSource Label: Source Group: Src Data: Larsen, A G, Sadolin, L Ø, Thomsen, T R & Oliveira, A S 2025, 'Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones', Sensors (Basel, Switzerland), vol. 25, no. 14, 4470. https://doi.org/10.3390/s25144470 – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subset Label: Collection Group: HoldingsInfo Data: Aalborg University (AAU): Publications / Aalborg Universitet: Publikationer – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Adult%22">Adult</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+health%22">Digital health</searchLink><br /><searchLink fieldCode="DE" term="%22Female%22">Female</searchLink><br /><searchLink fieldCode="DE" term="%22Gait+Analysis%2Fmethods%22">Gait Analysis/methods</searchLink><br /><searchLink fieldCode="DE" term="%22Gait+analysis%22">Gait analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Gait%2Fphysiology%22">Gait/physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Humans%22">Humans</searchLink><br /><searchLink fieldCode="DE" term="%22IMU%22">IMU</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Learning%22">Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Male%22">Male</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+Networks%22">Neural Networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer%22">Computer</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+monitoring%22">Remote monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Smartphone%22">Smartphone</searchLink><br /><searchLink fieldCode="DE" term="%22Walking%2Fphysiology%22">Walking/physiology</searchLink> – Name: Abstract Label: Description Group: Ab Data: As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: ISSN Label: ISSN Group: ISSN Data: 1424-8220<br />1424-8239 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: info:eu-repo/semantics/altIdentifier/pmid/40732598; info:eu-repo/semantics/altIdentifier/pissn/1424-8220; info:eu-repo/semantics/altIdentifier/eissn/1424-8239 – Name: DOI Label: DOI Group: ID Data: 10.3390/s25144470 – Name: URL Label: Availability Group: URL Data: https://vbn.aau.dk/da/publications/6903ab62-ca01-443d-bad2-48f0b7cd1c05<br />https://doi.org/10.3390/s25144470<br />https://vbn.aau.dk/ws/files/790776504/Open_Access_Article.pdf<br />https://www.scopus.com/pages/publications/105011535555 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ – Name: AN Label: Accession Number Group: ID Data: edsbas.611B7C11 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s25144470 Languages: – Text: English Subjects: – SubjectFull: Adult Type: general – SubjectFull: Digital health Type: general – SubjectFull: Female Type: general – SubjectFull: Gait Analysis/methods Type: general – SubjectFull: Gait analysis Type: general – SubjectFull: Gait/physiology Type: general – SubjectFull: Humans Type: general – SubjectFull: IMU Type: general – SubjectFull: Machine Learning Type: general – SubjectFull: Male Type: general – SubjectFull: Neural Networks Type: general – SubjectFull: Computer Type: general – SubjectFull: Remote monitoring Type: general – SubjectFull: Smartphone Type: general – SubjectFull: Walking/physiology Type: general Titles: – TitleFull: Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Larsen, Aske G – PersonEntity: Name: NameFull: Sadolin, Line Ø – PersonEntity: Name: NameFull: Thomsen, Trine R – PersonEntity: Name: NameFull: Oliveira, Anderson S IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14248220 – Type: issn-print Value: 14248239 – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa Titles: – TitleFull: Larsen, A G, Sadolin, L Ø, Thomsen, T R & Oliveira, A S 2025, 'Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones', Sensors (Basel, Switzerland), vol. 25, no. 14, 4470. https://doi.org/10.3390/s25144470 Type: main |
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