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Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository
Gomutbutra P, Kittisares A, Sanguansri A, Choosri N, Sawaddiruk P, Fakfum P, Lerttrakarnnon P, Saralamba S.. Frontiers in artificial intelligence, 2022-10-06
Topic: Risk, prevention and care in population studies
Data from 255 Thais with chronic pain were collected at Chiang Mai Medical School Hospital. After the patients self-rated their level of pain, a smartphone camera was used to capture faces for 10 s at a one-meter distance. For those unable to self-rate, a video recording was taken immediately after the move that causes the pain. The trained assistant rated each video clip for the pain assessment in advanced dementia (PAINAD). The pain was classified into three levels: mild, moderate, and severe. OpenFace © was used to convert the video clips into 18 facial action units (FAUs). Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Out of the models that only used FAU described in the literature (FAU 4, 6, 7, 9, 10, 25, 26, 27, and 45), multilayer perception is the most accurate, at 50%. The SVM model using FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, and 45, and gender had the best accuracy of 58% among the machine learning selection features. Our open-source experiment for automatically analyzing video clips for FAUs is not robust for classifying pain in the elderly. The consensus method to transform facial recognition algorithm values comparable to the human ratings, and international good practice for reciprocal sharing of data may improve the accuracy and feasibility of the machine learning's facial pain rater.
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APA 7th
Gomutbutra, P., Kittisares, A., Sanguansri, A., Choosri, N., Sawaddiruk, P., Fakfum, P., et al. (2022). Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository. Frontiers in artificial intelligence. https://doi.org/10.3389/frai.2022.942248
Vancouver
Gomutbutra P, Kittisares A, Sanguansri A, Choosri N, Sawaddiruk P, Fakfum P, et al. Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository. Frontiers in artificial intelligence. 2022. doi:10.3389/frai.2022.942248. PMID: 36277167.
BibTeX
@article{gomutbutra2022-36277167,
author = {Gomutbutra, P. and Kittisares, A. and Sanguansri, A. and Choosri, N. and Sawaddiruk, P. and Fakfum, P. and Lerttrakarnnon, P. and Saralamba, S.},
title = {{Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository}},
journal = {Frontiers in artificial intelligence},
year = {2022},
doi = {10.3389/frai.2022.942248},
pmid = {36277167},
url = {https://doi.org/10.3389/frai.2022.942248}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM).
- Out of the models that only used FAU described in the literature (FAU 4, 6, 7, 9, 10, 25, 26, 27, and 45), multilayer perception is the most accurate, at 50%.
- The SVM model using FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, and 45, and gender had the best accuracy of 58% among the machine learning selection features.
- Our open-source experiment for automatically analyzing video clips for FAUs is not robust for classifying pain in the elderly.
- The consensus method to transform facial recognition algorithm values comparable to the human ratings, and international good practice for reciprocal sharing of data may improve the accuracy and feasibility of the machine learning's facial pain rater.
Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 25 of the 58 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in artificial intelligence) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36277167) |
| Free to read | 2 / 2 | Free to read |
| Points | 25 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 25 of the 58 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
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