Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor
Postural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inerti...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2022/9483665 |
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| author | Kittichai Wantanajittikul Chakrit Wiboonsuntharangkoon Busaba Chuatrakoon Siriphan Kongsawasdi |
| author_facet | Kittichai Wantanajittikul Chakrit Wiboonsuntharangkoon Busaba Chuatrakoon Siriphan Kongsawasdi |
| author_sort | Kittichai Wantanajittikul |
| collection | DOAJ |
| description | Postural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; p<0.001). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89–0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement. |
| format | Article |
| id | doaj-art-53609b841aee4b6cbc8e5d1e1f576203 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-53609b841aee4b6cbc8e5d1e1f5762032025-02-03T01:32:35ZengWileyThe Scientific World Journal1537-744X2022-01-01202210.1155/2022/9483665Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial SensorKittichai Wantanajittikul0Chakrit Wiboonsuntharangkoon1Busaba Chuatrakoon2Siriphan Kongsawasdi3Department of Radiologic TechnologyResearch AdministrationDepartment of Physical TherapyDepartment of Physical TherapyPostural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; p<0.001). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89–0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement.http://dx.doi.org/10.1155/2022/9483665 |
| spellingShingle | Kittichai Wantanajittikul Chakrit Wiboonsuntharangkoon Busaba Chuatrakoon Siriphan Kongsawasdi Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor The Scientific World Journal |
| title | Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor |
| title_full | Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor |
| title_fullStr | Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor |
| title_full_unstemmed | Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor |
| title_short | Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor |
| title_sort | application of machine learning to predict trajectory of the center of pressure cop path of postural sway using a triaxial inertial sensor |
| url | http://dx.doi.org/10.1155/2022/9483665 |
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