Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults
In a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/930 |
| _version_ | 1797484774712934400 |
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| author | Kenshi Saho Masahiro Fujimoto Yoshiyuki Kobayashi Michito Matsumoto |
| author_facet | Kenshi Saho Masahiro Fujimoto Yoshiyuki Kobayashi Michito Matsumoto |
| author_sort | Kenshi Saho |
| collection | DOAJ |
| description | In a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk of falls. This study aimed to verify the effectiveness of our model by collecting actual MDR data from community-dwelling elderly people. First, MDR gait measurements were performed in a community setting, and the efficient gait parameters for the classification of fallers were extracted. Then, a support vector machine model that was trained and validated using the simulated MDR data was tested for the gait parameters extracted from the actual MDR data. A classification accuracy of 78.8% was achieved for the actual MDR data. The validity of the experimental results was confirmed based on a comparison with the results of our previous simulation study. Thus, the practicality of the faller classification model constructed using the simulated MDR data was verified for the actual MDR data. |
| first_indexed | 2024-03-09T23:10:16Z |
| format | Article |
| id | doaj.art-e51c6b6c58ba4b73844ac62e3c4dce75 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:10:16Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e51c6b6c58ba4b73844ac62e3c4dce752023-11-23T17:47:42ZengMDPI AGSensors1424-82202022-01-0122393010.3390/s22030930Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly AdultsKenshi Saho0Masahiro Fujimoto1Yoshiyuki Kobayashi2Michito Matsumoto3Department of Intelligent Robotics, Toyama Prefectural University, Imizu 939-0398, Toyama, JapanHuman Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Kashiwa 277-0882, Chiba, JapanHuman Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Kashiwa 277-0882, Chiba, JapanToyama College of Welfare Science, Imizu 939-0341, Toyama, JapanIn a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk of falls. This study aimed to verify the effectiveness of our model by collecting actual MDR data from community-dwelling elderly people. First, MDR gait measurements were performed in a community setting, and the efficient gait parameters for the classification of fallers were extracted. Then, a support vector machine model that was trained and validated using the simulated MDR data was tested for the gait parameters extracted from the actual MDR data. A classification accuracy of 78.8% was achieved for the actual MDR data. The validity of the experimental results was confirmed based on a comparison with the results of our previous simulation study. Thus, the practicality of the faller classification model constructed using the simulated MDR data was verified for the actual MDR data.https://www.mdpi.com/1424-8220/22/3/930micro-Doppler radargait measurementfall riskfaller classificationelderly peoplesupport vector machine |
| spellingShingle | Kenshi Saho Masahiro Fujimoto Yoshiyuki Kobayashi Michito Matsumoto Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults Sensors micro-Doppler radar gait measurement fall risk faller classification elderly people support vector machine |
| title | Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults |
| title_full | Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults |
| title_fullStr | Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults |
| title_full_unstemmed | Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults |
| title_short | Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults |
| title_sort | experimental verification of micro doppler radar measurements of fall risk related gait differences for community dwelling elderly adults |
| topic | micro-Doppler radar gait measurement fall risk faller classification elderly people support vector machine |
| url | https://www.mdpi.com/1424-8220/22/3/930 |
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