Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors
The study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in supine positions on forc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Biomechanics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-7078/2/4/41 |
| _version_ | 1797622230119612416 |
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| author | Rajat Emanuel Singh Jordan M. Fleury Sonu Gupta Nate P. Bachman Brent Alumbaugh Gannon White |
| author_facet | Rajat Emanuel Singh Jordan M. Fleury Sonu Gupta Nate P. Bachman Brent Alumbaugh Gannon White |
| author_sort | Rajat Emanuel Singh |
| collection | DOAJ |
| description | The study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in supine positions on force plates. We segmented their data into no-movement (NM) phases, i.e., the easy phase (EP) and IBM phase (comprising several events or sub-phases of IBM). Acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five–six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggles and were labeled as classes for classification using support vector machine (SVM), naive Bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had low computational costs and high robustness of 96–99% accuracy. |
| first_indexed | 2024-03-11T09:08:10Z |
| format | Article |
| id | doaj.art-9a41803dac4f42ca847ec241b9c2e626 |
| institution | Directory Open Access Journal |
| issn | 2673-7078 |
| language | English |
| last_indexed | 2024-03-11T09:08:10Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomechanics |
| spelling | doaj.art-9a41803dac4f42ca847ec241b9c2e6262023-11-16T19:15:04ZengMDPI AGBiomechanics2673-70782022-10-012452553710.3390/biomechanics2040041Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based SensorsRajat Emanuel Singh0Jordan M. Fleury1Sonu Gupta2Nate P. Bachman3Brent Alumbaugh4Gannon White5Department of Kinesiology, Northwestern College, Orange City, IA 51041, USADepartment of Kinesiology, Colorado Mesa University, Grand Junction, CO 81501, USADepartment of Computer Science, Northwestern College, Orange City, IA 51041, USADepartment of Kinesiology, Colorado Mesa University, Grand Junction, CO 81501, USADepartment of Kinesiology, Colorado Mesa University, Grand Junction, CO 81501, USADepartment of Kinesiology, Colorado Mesa University, Grand Junction, CO 81501, USAThe study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in supine positions on force plates. We segmented their data into no-movement (NM) phases, i.e., the easy phase (EP) and IBM phase (comprising several events or sub-phases of IBM). Acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five–six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggles and were labeled as classes for classification using support vector machine (SVM), naive Bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had low computational costs and high robustness of 96–99% accuracy.https://www.mdpi.com/2673-7078/2/4/41accelerationjerkinvoluntary breathing movementpattern recognitionclassification |
| spellingShingle | Rajat Emanuel Singh Jordan M. Fleury Sonu Gupta Nate P. Bachman Brent Alumbaugh Gannon White Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors Biomechanics acceleration jerk involuntary breathing movement pattern recognition classification |
| title | Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors |
| title_full | Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors |
| title_fullStr | Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors |
| title_full_unstemmed | Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors |
| title_short | Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors |
| title_sort | involuntary breathing movement pattern recognition and classification via force based sensors |
| topic | acceleration jerk involuntary breathing movement pattern recognition classification |
| url | https://www.mdpi.com/2673-7078/2/4/41 |
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