Currently submitted to: JMIR mHealth and uHealth
Date Submitted: Jun 1, 2026
Open Peer Review Period: Jun 4, 2026 - Jul 30, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Privacy-Preserving Mobile 3D Face Tracking for Objective Assessment of Empathic Facial Reactivity: Observational Study
ABSTRACT
Background:
Psychiatric and neurological conditions are often accompanied by alterations in empathic resonance with emotional states of others, yet objective measures of affective empathy remain limited in clinical practice. Mobile and privacy-preserving facial expression analysis can provide a scalable approach for assessing spontaneous empathic reactivity in research and clinical practice.
Objective:
This paper aimed to evaluate an established iOS-based, computationally efficient and privacy-preserving facial expression assessment of empathy using mobile 3D face tracking during established static and dynamic empathy paradigms.
Methods:
We integrated Apple ARKit-based 3D facial landmark extraction from an iPad True-Depth assisted camera into a multidimensional empathy assessment in 32 healthy adults. Participants viewed affect-laden pictures from the Multifaceted Empathy Test-core-2 and emotional film clips while facial blend shapes with semantically meaningful labels (eg, cheekSquint, eyeSquint) were extracted in real time. Linear mixed-effects models tested valence-specific reactivity, temporal modulation, task-demand effects, and associations with subjective empathy ratings, trait-empathy questionnaires, age, and sex. Hierarchical clustering examined co-activation patterns among blend shapes.
Results:
Positive stimuli elicited valence-specific facial reactivity, including increased smile-related activations (eg, mouthSmile: β=.241, P<.001) and reduced negative-valence expressions (eg, mouthFrown: β=.131, P<.001) during positive trials, consistent with emotional contagion. Reactivity showed dynamic temporal patterns, and film clips elicited stronger responses than static images for several positive-expression blend shapes. Hierarchical clustering revealed coherent blend shape co-activations. Facial reactivity was associated with subjective affective empathy ratings, empathic-trait questionnaires, and demographic variables.
Conclusions:
Mobile 3D face tracking captured subtle, spontaneous, and temporally dynamic facial reactivity during integrated empathy tasks while preserving privacy by avoiding raw video storage. This mHealth approach provides a scalable, clinically translatable framework for objective assessment of affective empathy and supports future implementation in neuropsychiatric research and mobile assessment.
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