KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery
Injuries and the associated surgery to the anterior cruciate ligament (ACL) can often trigger unpredictable effects, such as the so-called giving way effect, which is an uncontrolled buckling of the knee joint. For this purpose, the KneTex project has developed a smart textile-integrated sensor and...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2022-1101 |
| _version_ | 1811203725365084160 |
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| author | Cramer Lukas Yavuz Sinan Schlage Nana Mühlen Andreas Kitzig Andreas Naroska Edwin Stockmanns Gudrun |
| author_facet | Cramer Lukas Yavuz Sinan Schlage Nana Mühlen Andreas Kitzig Andreas Naroska Edwin Stockmanns Gudrun |
| author_sort | Cramer Lukas |
| collection | DOAJ |
| description | Injuries and the associated surgery to the anterior cruciate ligament (ACL) can often trigger unpredictable effects, such as the so-called giving way effect, which is an uncontrolled buckling of the knee joint. For this purpose, the KneTex project has developed a smart textile-integrated sensor and actuator bandage system to record the movement of such patients and to monitor and support the rehabilitation process. Long-term monitoring and analysis of the movement data will identify patterns or gait types that can lead to a giving way effect. This paper describes the recent developments of the random forest model-based motion classification system developed within the project. Improvements have been achieved by reducing the number of features needed by 25% using feature importance analysis, speeding up the computation time by 14%, and increasing the classification efficiency. Feature elimination is a useful tool to improve classification systems in settings where feature count is high and feature importance analysis contributed by improving our understanding which sensor of our system are important for the motion classification task. |
| first_indexed | 2024-04-12T02:59:47Z |
| format | Article |
| id | doaj.art-b47505092d6d4848aba2e15adcdee9f3 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-12T02:59:47Z |
| publishDate | 2022-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-b47505092d6d4848aba2e15adcdee9f32022-12-22T03:50:41ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042022-09-018239639910.1515/cdbme-2022-1101KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgeryCramer Lukas0Yavuz Sinan1Schlage Nana2Mühlen Andreas3Kitzig Andreas4Naroska Edwin5Stockmanns Gudrun6Niederrhein University of Applied Sciences, Reinarzstr. 49,Krefeld, GermanyNiederrhein University of Applied Sciences,Krefeld, GermanyNiederrhein University of Applied Sciences,Krefeld, GermanyNiederrhein University of Applied Sciences,Krefeld, GermanyNiederrhein University of Applied Sciences,Krefeld, GermanyNiederrhein University of Applied Sciences,Krefeld, GermanyNiederrhein University of Applied Sciences,Krefeld, GermanyInjuries and the associated surgery to the anterior cruciate ligament (ACL) can often trigger unpredictable effects, such as the so-called giving way effect, which is an uncontrolled buckling of the knee joint. For this purpose, the KneTex project has developed a smart textile-integrated sensor and actuator bandage system to record the movement of such patients and to monitor and support the rehabilitation process. Long-term monitoring and analysis of the movement data will identify patterns or gait types that can lead to a giving way effect. This paper describes the recent developments of the random forest model-based motion classification system developed within the project. Improvements have been achieved by reducing the number of features needed by 25% using feature importance analysis, speeding up the computation time by 14%, and increasing the classification efficiency. Feature elimination is a useful tool to improve classification systems in settings where feature count is high and feature importance analysis contributed by improving our understanding which sensor of our system are important for the motion classification task.https://doi.org/10.1515/cdbme-2022-1101kneteximusmotion classificationrecognition systemfeature importancerandom forest |
| spellingShingle | Cramer Lukas Yavuz Sinan Schlage Nana Mühlen Andreas Kitzig Andreas Naroska Edwin Stockmanns Gudrun KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery Current Directions in Biomedical Engineering knetex imus motion classification recognition system feature importance random forest |
| title | KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery |
| title_full | KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery |
| title_fullStr | KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery |
| title_full_unstemmed | KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery |
| title_short | KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery |
| title_sort | knetex improvements to classification methods for a sensor system for rehabilitation after acl surgery |
| topic | knetex imus motion classification recognition system feature importance random forest |
| url | https://doi.org/10.1515/cdbme-2022-1101 |
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