Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors
The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6931 |
| _version_ | 1797551866272284672 |
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| author | Chia-Hsuan Lee Chi-Han Wu Bernard C. Jiang Tien-Lung Sun |
| author_facet | Chia-Hsuan Lee Chi-Han Wu Bernard C. Jiang Tien-Lung Sun |
| author_sort | Chia-Hsuan Lee |
| collection | DOAJ |
| description | The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach’s decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the <i>Z</i> axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results. |
| first_indexed | 2024-03-10T15:52:11Z |
| format | Article |
| id | doaj.art-db75b0a868124427a4ca8973ac1c27ec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:52:11Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-db75b0a868124427a4ca8973ac1c27ec2023-11-20T15:57:56ZengMDPI AGApplied Sciences2076-34172020-10-011019693110.3390/app10196931Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection SensorsChia-Hsuan Lee0Chi-Han Wu1Bernard C. Jiang2Tien-Lung Sun3Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, TaiwanDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, TaiwanDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320, TaiwanThe results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach’s decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the <i>Z</i> axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results.https://www.mdpi.com/2076-3417/10/19/6931fall riskcommunity serviceinertial sensormultiple fall risk assessment |
| spellingShingle | Chia-Hsuan Lee Chi-Han Wu Bernard C. Jiang Tien-Lung Sun Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors Applied Sciences fall risk community service inertial sensor multiple fall risk assessment |
| title | Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors |
| title_full | Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors |
| title_fullStr | Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors |
| title_full_unstemmed | Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors |
| title_short | Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors |
| title_sort | exploration of fall evaluation scores using clinical tools with the short form berg balance scale and timed up and go and motion detection sensors |
| topic | fall risk community service inertial sensor multiple fall risk assessment |
| url | https://www.mdpi.com/2076-3417/10/19/6931 |
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