Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment
This study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was develo...
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| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/7044 |
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| author | Yasuhirio Akiyama Kyogo Kazumura Shogo Okamoto Yoji Yamada |
| author_facet | Yasuhirio Akiyama Kyogo Kazumura Shogo Okamoto Yoji Yamada |
| author_sort | Yasuhirio Akiyama |
| collection | DOAJ |
| description | This study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was developed to estimate the MoS from IMU acceleration time-series data. The relationship between MoS and other stability indices, such as the Lyapunov exponent and the multi-site time-series (MSTS) index, using data from five IMU sensors placed on various body parts was also examined. To simulate diverse gait conditions, treadmill speed was varied, and a knee–ankle–foot orthosis was used to restrict left knee extension, inducing gait asymmetry. The model achieved over 90% accuracy in classifying MoS in both forward and lateral directions using three-axis acceleration data from the IMUs. However, the correlation between MoS and the Lyapunov exponent or MSTS index was weak, suggesting that these indices may capture different aspects of gait stability. |
| format | Article |
| id | doaj-art-9d99c165b32d4d4eb03cda45d5182824 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9d99c165b32d4d4eb03cda45d51828242025-08-20T02:14:16ZengMDPI AGSensors1424-82202024-10-012421704410.3390/s24217044Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait ExperimentYasuhirio Akiyama0Kyogo Kazumura1Shogo Okamoto2Yoji Yamada3Faculty of Textile Science and Technology, Shinshu University, Nagano 386-8567, JapanDepartment of Mechanical Systems Engineering, Nagoya University, Aichi 464-8603, JapanDepartment of Computer Science, Tokyo Metropolitan University, Tokyo 191-0065, JapanNational Institute of Technology, Toyota College, Toyota 471-0067, JapanThis study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was developed to estimate the MoS from IMU acceleration time-series data. The relationship between MoS and other stability indices, such as the Lyapunov exponent and the multi-site time-series (MSTS) index, using data from five IMU sensors placed on various body parts was also examined. To simulate diverse gait conditions, treadmill speed was varied, and a knee–ankle–foot orthosis was used to restrict left knee extension, inducing gait asymmetry. The model achieved over 90% accuracy in classifying MoS in both forward and lateral directions using three-axis acceleration data from the IMUs. However, the correlation between MoS and the Lyapunov exponent or MSTS index was weak, suggesting that these indices may capture different aspects of gait stability.https://www.mdpi.com/1424-8220/24/21/7044gait stabilitymargin of stabilityLyapunov exponentinertial measurement unitconvolutional neural network |
| spellingShingle | Yasuhirio Akiyama Kyogo Kazumura Shogo Okamoto Yoji Yamada Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment Sensors gait stability margin of stability Lyapunov exponent inertial measurement unit convolutional neural network |
| title | Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment |
| title_full | Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment |
| title_fullStr | Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment |
| title_full_unstemmed | Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment |
| title_short | Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment |
| title_sort | utilizing inertial measurement units for detecting dynamic stability variations in a multi condition gait experiment |
| topic | gait stability margin of stability Lyapunov exponent inertial measurement unit convolutional neural network |
| url | https://www.mdpi.com/1424-8220/24/21/7044 |
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