Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones
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....
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4470 |
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| author | Aske G. Larsen Line Ø. Sadolin Trine R. Thomsen Anderson S. Oliveira |
| author_facet | Aske G. Larsen Line Ø. Sadolin Trine R. Thomsen Anderson S. Oliveira |
| author_sort | Aske G. Larsen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-543b1d142e4b4ac6816f6c8c048f6fdb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-543b1d142e4b4ac6816f6c8c048f6fdb2025-08-20T03:56:47ZengMDPI AGSensors1424-82202025-07-012514447010.3390/s25144470Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of SmartphonesAske G. Larsen0Line Ø. Sadolin1Trine R. Thomsen2Anderson S. Oliveira3Department of Chemistry and Bioscience, Aalborg University, DK-9220 Aalborg Oest, DenmarkDepartment of Chemistry and Bioscience, Aalborg University, DK-9220 Aalborg Oest, DenmarkDepartment of Chemistry and Bioscience, Aalborg University, DK-9220 Aalborg Oest, DenmarkDepartment of Materials and Production, Aalborg University, DK-9220 Aalborg Oest, DenmarkAs 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.https://www.mdpi.com/1424-8220/25/14/4470remote monitoringdigital healthmachine learninggait analysissmartphoneIMU |
| spellingShingle | Aske G. Larsen Line Ø. Sadolin Trine R. Thomsen Anderson S. Oliveira Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones Sensors remote monitoring digital health machine learning gait analysis smartphone IMU |
| title | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
| title_full | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
| title_fullStr | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
| title_full_unstemmed | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
| title_short | Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones |
| title_sort | extraction of clinically relevant temporal gait parameters from imu sensors mimicking the use of smartphones |
| topic | remote monitoring digital health machine learning gait analysis smartphone IMU |
| url | https://www.mdpi.com/1424-8220/25/14/4470 |
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