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Vision-based estimation of fatigue and engagement in cognitive training sessions
Wang Y, Turnbull A, Xu Y, Heffner K, Lin FV, Adeli E.. Artificial intelligence in medicine, 2024-06-27
Topic: Risk, prevention and care in population studies
Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. The effectiveness of CCT is often affected by a lack of effective engagement. Mental fatigue is a the primary factor for compromising effective engagement in CCT, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can constantly monitor and reliably detect mental fatigue during CCT. Here, we develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring real-time mental fatigue in older adults with mild cognitive impairment using their video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy (79.58%) and precision (0.82) compared to the prior models for binary and multi-class classification of mental fatigue. We also validated our model by significantly relating to reaction time across CCT tasks (Waldχ 2 =5.16,p=0.023). By leveraging dynamic temporal information, the RVT model demonstrates the potential to accurately measure real-time mental fatigue, laying the foundation for future CCT research aiming to enhance effective engagement by timely prevention of mental fatigue.
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APA 7th
Wang, Y., Turnbull, A., Xu, Y., Heffner, K., Lin, F. V., & Adeli, E. (2024). Vision-based estimation of fatigue and engagement in cognitive training sessions. Artificial intelligence in medicine. https://doi.org/10.1016/j.artmed.2024.102923
Vancouver
Wang Y, Turnbull A, Xu Y, Heffner K, Lin FV, Adeli E. Vision-based estimation of fatigue and engagement in cognitive training sessions. Artificial intelligence in medicine. 2024. doi:10.1016/j.artmed.2024.102923. PMID: 38970987.
BibTeX
@article{wang2024-38970987,
author = {Wang, Y. and Turnbull, A. and Xu, Y. and Heffner, K. and Lin, F. V. and Adeli, E.},
title = {{Vision-based estimation of fatigue and engagement in cognitive training sessions}},
journal = {Artificial intelligence in medicine},
year = {2024},
doi = {10.1016/j.artmed.2024.102923},
pmid = {38970987},
url = {https://doi.org/10.1016/j.artmed.2024.102923}
}
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Key findings
- The RVT model achieved the highest balanced accuracy (79.58%) and precision (0.82) compared to the prior models for binary and multi-class classification of mental fatigue.
- We also validated our model by significantly relating to reaction time across CCT tasks (Waldχ 2 =5.16,p=0.023).
Study details
- Effect size79.58%
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, 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 26 of the 58 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Artificial intelligence in medicine) |
| 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 | 4 / 5 | 2024 — within 3 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 38970987) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 26 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
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