Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones
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| Názov: | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
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| Autori: | Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen, Anderson S. Oliveira |
| Zdroj: | Sensors (Basel) Sensors, Vol 25, Iss 14, p 4470 (2025) 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 |
| Informácie o vydavateľovi: | MDPI AG, 2025. |
| Rok vydania: | 2025 |
| Predmety: | Adult, Male, Chemical technology, Walking/physiology, digital health, Gait Analysis/methods, TP1-1185, smartphone, IMU, Article, Machine Learning, Remote monitoring, machine learning, gait analysis, Humans, Female, Gait analysis, Neural Networks, Computer, Smartphone, Gait/physiology, Digital health, remote monitoring |
| Popis: | 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 (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. |
| Druh dokumentu: | Article Other literature type |
| Popis súboru: | application/pdf |
| Jazyk: | English |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s25144470 |
| Prístupová URL adresa: | https://doaj.org/article/543b1d142e4b4ac6816f6c8c048f6fdb https://vbn.aau.dk/da/publications/6903ab62-ca01-443d-bad2-48f0b7cd1c05 https://vbn.aau.dk/ws/files/790776504/Open_Access_Article.pdf http://www.scopus.com/inward/record.url?scp=105011535555&partnerID=8YFLogxK https://doi.org/10.3390/s25144470 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi.dedup.....e9f52786f3085ef13981b86a8b014c9a |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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 (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. |
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| ISSN: | 14248220 |
| DOI: | 10.3390/s25144470 |