Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty
Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown prom...
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/16/4385 |
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| author | Carlo Dindorf Wolfgang Teufl Bertram Taetz Gabriele Bleser Michael Fröhlich |
| author_facet | Carlo Dindorf Wolfgang Teufl Bertram Taetz Gabriele Bleser Michael Fröhlich |
| author_sort | Carlo Dindorf |
| collection | DOAJ |
| description | Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy M<sub>acc</sub> = 100%), followed by features based on simple descriptive statistics (M<sub>acc</sub> = 97.38%) and waveform data (M<sub>acc</sub> = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns. |
| first_indexed | 2024-03-10T17:53:31Z |
| format | Article |
| id | doaj.art-195e71f9c5c04f65bcd6b1a96dcb8007 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:53:31Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-195e71f9c5c04f65bcd6b1a96dcb80072023-11-20T09:16:01ZengMDPI AGSensors1424-82202020-08-012016438510.3390/s20164385Interpretability of Input Representations for Gait Classification in Patients after Total Hip ArthroplastyCarlo Dindorf0Wolfgang Teufl1Bertram Taetz2Gabriele Bleser3Michael Fröhlich4Department of Sports Science, Technische Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, 67663 Kaiserslautern, GermanyJunior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, GermanyJunior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, GermanyJunior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, GermanyDepartment of Sports Science, Technische Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, 67663 Kaiserslautern, GermanyMany machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy M<sub>acc</sub> = 100%), followed by features based on simple descriptive statistics (M<sub>acc</sub> = 97.38%) and waveform data (M<sub>acc</sub> = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns.https://www.mdpi.com/1424-8220/20/16/4385explainable artificial intelligenceinertial measurement unitmachine learningbiomechanicsgaittotal hip replacement |
| spellingShingle | Carlo Dindorf Wolfgang Teufl Bertram Taetz Gabriele Bleser Michael Fröhlich Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty Sensors explainable artificial intelligence inertial measurement unit machine learning biomechanics gait total hip replacement |
| title | Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty |
| title_full | Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty |
| title_fullStr | Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty |
| title_full_unstemmed | Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty |
| title_short | Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty |
| title_sort | interpretability of input representations for gait classification in patients after total hip arthroplasty |
| topic | explainable artificial intelligence inertial measurement unit machine learning biomechanics gait total hip replacement |
| url | https://www.mdpi.com/1424-8220/20/16/4385 |
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