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Article: Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor
Title | Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor |
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Authors | |
Keywords | deep learning convolutional neural network running kinematics wearable sensor |
Issue Date | 2021 |
Publisher | Molecular Diversity Preservation International. The Journal's web site is located at http://www.mdpi.net/sensors |
Citation | Sensors, 2021, v. 21 n. 14, p. article no. 4633 How to Cite? |
Abstract | Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor. |
Persistent Identifier | http://hdl.handle.net/10722/301165 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 0.786 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chow, DHK | - |
dc.contributor.author | Tremblay, L | - |
dc.contributor.author | Lam, CY | - |
dc.contributor.author | Yeung, AWY | - |
dc.contributor.author | Cheng, WHW | - |
dc.contributor.author | Tse, PTW | - |
dc.date.accessioned | 2021-07-27T08:07:04Z | - |
dc.date.available | 2021-07-27T08:07:04Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Sensors, 2021, v. 21 n. 14, p. article no. 4633 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301165 | - |
dc.description.abstract | Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor. | - |
dc.language | eng | - |
dc.publisher | Molecular Diversity Preservation International. The Journal's web site is located at http://www.mdpi.net/sensors | - |
dc.relation.ispartof | Sensors | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | deep learning | - |
dc.subject | convolutional neural network | - |
dc.subject | running | - |
dc.subject | kinematics | - |
dc.subject | wearable sensor | - |
dc.title | Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor | - |
dc.type | Article | - |
dc.identifier.email | Lam, CY: lamclive@hku.hk | - |
dc.identifier.authority | Lam, CY=rp02771 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/s21144633 | - |
dc.identifier.pmid | 34300372 | - |
dc.identifier.pmcid | PMC8309515 | - |
dc.identifier.scopus | eid_2-s2.0-85109007395 | - |
dc.identifier.hkuros | 323571 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 14 | - |
dc.identifier.spage | article no. 4633 | - |
dc.identifier.epage | article no. 4633 | - |
dc.identifier.isi | WOS:000677061100001 | - |
dc.publisher.place | Switzerland | - |