Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy
Ground reaction force (GRF) components can be estimated using insole pressure sensors. Principal component analysis in conjunction with machine learning (PCA-ML) methods are widely used for this task. PCA reduces dimensionality and requires pre-normalization. In this paper, we evaluated the impact o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1137 |
| _version_ | 1797297001715466240 |
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| author | Amal Kammoun Philippe Ravier Olivier Buttelli |
| author_facet | Amal Kammoun Philippe Ravier Olivier Buttelli |
| author_sort | Amal Kammoun |
| collection | DOAJ |
| description | Ground reaction force (GRF) components can be estimated using insole pressure sensors. Principal component analysis in conjunction with machine learning (PCA-ML) methods are widely used for this task. PCA reduces dimensionality and requires pre-normalization. In this paper, we evaluated the impact of twelve pre-normalization methods using three PCA-ML methods on the accuracy of GRF component estimation. Accuracy was assessed using laboratory data from gold-standard force plate measurements. Data were collected from nine subjects during slow- and normal-speed walking activities. We tested the ANN (artificial neural network) and LS (least square) methods while also exploring support vector regression (SVR), a method not previously examined in the literature, to the best of our knowledge. In the context of our work, our results suggest that the same normalization method can produce the worst or the best accuracy results, depending on the ML method. For example, the body weight normalization method yields good results for PCA-ANN but the worst performance for PCA-SVR. For PCA-ANN and PCA-LS, the vector standardization normalization method is recommended. For PCA-SVR, the mean method is recommended. The final message is not to define a normalization method a priori independently of the ML method. |
| first_indexed | 2024-03-07T22:14:54Z |
| format | Article |
| id | doaj.art-60d20a71fe004c548795cc28746fc129 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:14:54Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-60d20a71fe004c548795cc28746fc1292024-02-23T15:33:40ZengMDPI AGSensors1424-82202024-02-01244113710.3390/s24041137Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation AccuracyAmal Kammoun0Philippe Ravier1Olivier Buttelli2PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, FrancePRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, FrancePRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, FranceGround reaction force (GRF) components can be estimated using insole pressure sensors. Principal component analysis in conjunction with machine learning (PCA-ML) methods are widely used for this task. PCA reduces dimensionality and requires pre-normalization. In this paper, we evaluated the impact of twelve pre-normalization methods using three PCA-ML methods on the accuracy of GRF component estimation. Accuracy was assessed using laboratory data from gold-standard force plate measurements. Data were collected from nine subjects during slow- and normal-speed walking activities. We tested the ANN (artificial neural network) and LS (least square) methods while also exploring support vector regression (SVR), a method not previously examined in the literature, to the best of our knowledge. In the context of our work, our results suggest that the same normalization method can produce the worst or the best accuracy results, depending on the ML method. For example, the body weight normalization method yields good results for PCA-ANN but the worst performance for PCA-SVR. For PCA-ANN and PCA-LS, the vector standardization normalization method is recommended. For PCA-SVR, the mean method is recommended. The final message is not to define a normalization method a priori independently of the ML method.https://www.mdpi.com/1424-8220/24/4/1137insole measurementforce plate measurementGRF component estimationnormalization methodsmachine learningPCA pre-normalization |
| spellingShingle | Amal Kammoun Philippe Ravier Olivier Buttelli Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy Sensors insole measurement force plate measurement GRF component estimation normalization methods machine learning PCA pre-normalization |
| title | Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy |
| title_full | Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy |
| title_fullStr | Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy |
| title_full_unstemmed | Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy |
| title_short | Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy |
| title_sort | impact of pca pre normalization methods on ground reaction force estimation accuracy |
| topic | insole measurement force plate measurement GRF component estimation normalization methods machine learning PCA pre-normalization |
| url | https://www.mdpi.com/1424-8220/24/4/1137 |
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