Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification
Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7705 |
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| author | Zhuofu Liu Zihao Shu Vincenzo Cascioli Peter W. McCarthy |
| author_facet | Zhuofu Liu Zihao Shu Vincenzo Cascioli Peter W. McCarthy |
| author_sort | Zhuofu Liu |
| collection | DOAJ |
| description | Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training; 0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection. |
| format | Article |
| id | doaj-art-559746b30dc04bec96f6d45d4ff4fad5 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-559746b30dc04bec96f6d45d4ff4fad52025-08-20T02:50:41ZengMDPI AGSensors1424-82202024-12-012423770510.3390/s24237705Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture ClassificationZhuofu Liu0Zihao Shu1Vincenzo Cascioli2Peter W. McCarthy3The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, ChinaThe Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, ChinaMurdoch University Chiropractic Clinic, Murdoch University, Murdoch 6150, AustraliaFaculty of Life Science and Education, University of South Wales, Treforest, Pontypridd CF37 1DL, UKSedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training; 0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.https://www.mdpi.com/1424-8220/24/23/7705sitting postureforce-sensitive resistortriaxial accelerometersclassification algorithmsensor verificationaccuracy |
| spellingShingle | Zhuofu Liu Zihao Shu Vincenzo Cascioli Peter W. McCarthy Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification Sensors sitting posture force-sensitive resistor triaxial accelerometers classification algorithm sensor verification accuracy |
| title | Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification |
| title_full | Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification |
| title_fullStr | Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification |
| title_full_unstemmed | Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification |
| title_short | Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification |
| title_sort | comparative analysis of force sensitive resistors and triaxial accelerometers for sitting posture classification |
| topic | sitting posture force-sensitive resistor triaxial accelerometers classification algorithm sensor verification accuracy |
| url | https://www.mdpi.com/1424-8220/24/23/7705 |
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