Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study
Notice bibliographique
Résumé
Background: The prevalence of dementia is expected to rise with an aging population, necessitating accessible early detection methods. Serious games have emerged as potential cognitive screening tools. They provide not only an engaging platform for assessing cognitive function but also serve as valuable indicators of cognitive health through engagement levels observed during play. Objective: This study aims to examine the differences in engagement-related behaviors between older adults with and without dementia during serious gaming sessions. Further, it seeks to identify the key contributors that enhance the effectiveness of machine learning for dementia classification based on engagement-related behaviors. Methods: This was an exploratory proof-of-concept study. Over 8 weeks, 20 older adults, 6 of whom were living with dementia, were enrolled in a single-case design study. Participants played 1 of 4 "Vibrant Minds" serious games (Bejeweled, Whack-A-Mole, Mah-jong, and Word-Search) over 8 weeks (16 30-min sessions). Throughout the study, sessions were recorded to analyze engagement-related behaviors. This paper reports on the analysis of the engagement-related behaviors of 15 participants. The videos of these 15 participants (10 cognitively intact, 5 with dementia) were analyzed by 2 independent raters, individually annotating engagement-related behaviors at 15-second intervals using a coding system. This analysis resulted in 1774 data points categorized into 47 behavior codes, augmented by 54 additional features including personal characteristics, technical issues, and environmental factors. Each engagement-related behavior was compared between older adults living with dementia and older adults without dementia using the χ² test with a 2×2 contingency table with a significance level of .05. Codes underwent one-hot encoding and were processed using random forest classifiers to distinguish between participant groups. Results: Significant differences in 64% of engagement-related behaviors were found between groups, notably in torso movements, voice modulation, facial expressions, and concentration. Including engagement-related behaviors, environmental disturbances, technical issues, and personal characteristics resulted in the best model for classifying cases of dementia correctly, achieving an F1-score of 0.91 (95% CI 0.851-0.963) and an area under the receiver operating curve of 0.99 (95% CI 0.984-1.000). Conclusions: Key features distinguishing between older adults with and without dementia during serious gameplay included torso, voice, facial, and concentration behaviors, as well as age. The best performing machine learning model identified included features of engagement-related behavios, environmental disturbances, technical challenges, and personal attributes. Engagement-related behaviors observed during serious gaming offer crucial markers for identifying dementia. Machine learning models that incorporate these unique behavioral markers present a promising, noninvasive approach for early dementia screening in a variety of settings.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».