Automatic Detection of Pain Intensity. Hammal, Z. & Cohn, J. F. In Proceedings of the 14th ACM International Conference on Multimodal Interaction, of ICMI '12, pages 47--52, New York, NY, USA, 2012. ACM. ![Automatic Detection of Pain Intensity [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Previous efforts suggest that occurrence of pain can be detected from the face. Can intensity of pain be detected as well? The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. F1 for each level of pain intensity ranged from 91% to 96% and from 40% to 67% for 5-folds and leave-one-subject-out cross-validation, respectively. Intra-class correlation, which assesses the consistency of continuous pain intensity between manual and automatic PSPI was 0.85 and 0.55 for 5-folds and leave-one-subject-out cross-validation, respectively, which suggests moderate to high consistency. These findings show that pain intensity can be reliably measured from facial expression in participants with orthopedic injury.
@inproceedings{hammal_automatic_2012,
address = {New York, NY, USA},
series = {{ICMI} '12},
title = {Automatic {Detection} of {Pain} {Intensity}},
isbn = {978-1-4503-1467-1},
url = {http://doi.acm.org/10.1145/2388676.2388688},
doi = {10.1145/2388676.2388688},
abstract = {Previous efforts suggest that occurrence of pain can be detected from the face. Can intensity of pain be detected as well? The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. F1 for each level of pain intensity ranged from 91\% to 96\% and from 40\% to 67\% for 5-folds and leave-one-subject-out cross-validation, respectively. Intra-class correlation, which assesses the consistency of continuous pain intensity between manual and automatic PSPI was 0.85 and 0.55 for 5-folds and leave-one-subject-out cross-validation, respectively, which suggests moderate to high consistency. These findings show that pain intensity can be reliably measured from facial expression in participants with orthopedic injury.},
urldate = {2014-06-05TZ},
booktitle = {Proceedings of the 14th {ACM} {International} {Conference} on {Multimodal} {Interaction}},
publisher = {ACM},
author = {Hammal, Zakia and Cohn, Jeffrey F.},
year = {2012},
pages = {47--52}
}
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Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. F1 for each level of pain intensity ranged from 91% to 96% and from 40% to 67% for 5-folds and leave-one-subject-out cross-validation, respectively. Intra-class correlation, which assesses the consistency of continuous pain intensity between manual and automatic PSPI was 0.85 and 0.55 for 5-folds and leave-one-subject-out cross-validation, respectively, which suggests moderate to high consistency. 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The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. 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