Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database
In general, classification tasks can differ significantly in their task complexity. For instance, image-based differentiation between vehicles and pedestrians is most likely expected to be less complex than CT-scan-based differentiation between several lung diseases. Intuitively, based on a human po...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9900327/ |
| _version_ | 1826938429641850880 |
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| author | Peter Bellmann Patrick Thiam Hans A. Kestler Friedhelm Schwenker |
| author_facet | Peter Bellmann Patrick Thiam Hans A. Kestler Friedhelm Schwenker |
| author_sort | Peter Bellmann |
| collection | DOAJ |
| description | In general, classification tasks can differ significantly in their task complexity. For instance, image-based differentiation between vehicles and pedestrians is most likely expected to be less complex than CT-scan-based differentiation between several lung diseases. Intuitively, based on a human point of view, one can identify some classification tasks as more complex than other classification tasks. Moreover, based on expert knowledge and/or task-specific meta information, one could attempt to estimate the complexity ranks of specific classification tasks. In this work, based on the publicly available BioVid Heat Pain Database (BVDB), we experimentally confirm the intuitive assumption that the task of automated pain intensity recognition (PIR) is very challenging. Inspired by the field of chaos theory, we show that the BVDB-specific PIR task can not only be seen as highly complex, but is even identified as a classification task of chaotic nature. To this end, we apply Hao’s working definition for chaotic systems and provide an experiment-based chaos check method. To validate our approach, as a non-complex counterpart, we include a task of handwritten numerals distinction. Our study provides two main contributions, i.e.: i) an enhanced understanding for the still present and – more importantly – substantial gap between the ground truth and the predictions reported by different research groups in combination with automated PIR tasks; and ii) an approach for a numerical complexity check based on chaos theory. Different research directions are discussed for future work. Note that improving PIR accuracy performance is not part of the study objective. |
| first_indexed | 2024-04-11T10:30:59Z |
| format | Article |
| id | doaj.art-07e35d6a0f5945aab6d5f9accff0af37 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-02-17T18:57:49Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-07e35d6a0f5945aab6d5f9accff0af372024-12-11T00:02:51ZengIEEEIEEE Access2169-35362022-01-011010277010277710.1109/ACCESS.2022.32089059900327Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain DatabasePeter Bellmann0https://orcid.org/0000-0003-0182-4469Patrick Thiam1https://orcid.org/0000-0002-6769-8410Hans A. Kestler2https://orcid.org/0000-0002-4759-5254Friedhelm Schwenker3https://orcid.org/0000-0001-5118-0812Institute of Neural Information Processing, Ulm University, Ulm, GermanyInstitute of Neural Information Processing, Ulm University, Ulm, GermanyInstitute of Medical Systems Biology, Ulm University, Ulm, GermanyInstitute of Neural Information Processing, Ulm University, Ulm, GermanyIn general, classification tasks can differ significantly in their task complexity. For instance, image-based differentiation between vehicles and pedestrians is most likely expected to be less complex than CT-scan-based differentiation between several lung diseases. Intuitively, based on a human point of view, one can identify some classification tasks as more complex than other classification tasks. Moreover, based on expert knowledge and/or task-specific meta information, one could attempt to estimate the complexity ranks of specific classification tasks. In this work, based on the publicly available BioVid Heat Pain Database (BVDB), we experimentally confirm the intuitive assumption that the task of automated pain intensity recognition (PIR) is very challenging. Inspired by the field of chaos theory, we show that the BVDB-specific PIR task can not only be seen as highly complex, but is even identified as a classification task of chaotic nature. To this end, we apply Hao’s working definition for chaotic systems and provide an experiment-based chaos check method. To validate our approach, as a non-complex counterpart, we include a task of handwritten numerals distinction. Our study provides two main contributions, i.e.: i) an enhanced understanding for the still present and – more importantly – substantial gap between the ground truth and the predictions reported by different research groups in combination with automated PIR tasks; and ii) an approach for a numerical complexity check based on chaos theory. Different research directions are discussed for future work. Note that improving PIR accuracy performance is not part of the study objective.https://ieeexplore.ieee.org/document/9900327/BioVid heat pain databasechaos theoryclassification task complexitydecision treespain intensity recognitionphysiological signals |
| spellingShingle | Peter Bellmann Patrick Thiam Hans A. Kestler Friedhelm Schwenker Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database IEEE Access BioVid heat pain database chaos theory classification task complexity decision trees pain intensity recognition physiological signals |
| title | Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database |
| title_full | Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database |
| title_fullStr | Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database |
| title_full_unstemmed | Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database |
| title_short | Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database |
| title_sort | machine learning based pain intensity estimation where pattern recognition meets chaos theory x2014 an example based on the biovid heat pain database |
| topic | BioVid heat pain database chaos theory classification task complexity decision trees pain intensity recognition physiological signals |
| url | https://ieeexplore.ieee.org/document/9900327/ |
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