Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum
| العنوان: | Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum |
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| المؤلفون: | Myunghyun Lee, Sukyung Park |
| المصدر: | Sensors (Basel, Switzerland) Sensors Volume 20 Issue 21 Sensors, Vol 20, Iss 6277, p 6277 (2020) |
| بيانات النشر: | MDPI, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Sacrum, Computer science, medicine.medical_treatment, Kinematics, Walking, lcsh:Chemical technology, spring mechanics, Biochemistry, Lower limb, Article, biomechanics, Analytical Chemistry, Machine Learning, 03 medical and health sciences, Wearable Electronic Devices, 0302 clinical medicine, Center of pressure (terrestrial locomotion), Inertial measurement unit, center of pressure, medicine, Torque, Humans, lcsh:TP1-1185, Electrical and Electronic Engineering, Ground reaction force, ground reaction forces, Instrumentation, Simulation, Rehabilitation, Biomechanics, joint torques, 030229 sport sciences, Gait, Atomic and Molecular Physics, and Optics, Biomechanical Phenomena, Kinetics, wearables, Lower Extremity, Gait Analysis, human activities, 030217 neurology & neurosurgery, three dimensions |
| الوصف: | Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. The GRF and center of pressure (CoP) have a mechanical relationship with the center of mass (CoM) in the three dimensions (3D). This biomechanical characteristic can be used to establish an appropriate input and structure of an ANN. In this study, an ANN for estimating gait kinetics with a single inertial measurement unit (IMU) was designed the kinematics of the IMU placed on the sacrum as a proxy for the CoM kinematics were applied based on the 3D spring mechanics. The walking data from 17 participants walking at various speeds were used to train and validate the ANN. The estimated 3D GRF, CoP trajectory, and joint torques of the lower limbs were reasonably accurate, with normalized root-mean-square errors (NRMSEs) of 6.7% to 15.6%, 8.2% to 20.0%, and 11.4% to 24.1%, respectively. This result implies that the biomechanical characteristics can be used to estimate the complete three-dimensional gait data with an ANN model and a single IMU. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a46c3f5f03c9d806e3318fd4cc6d296cTest http://europepmc.org/articles/PMC7663495Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....a46c3f5f03c9d806e3318fd4cc6d296c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14248220 |
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