- Studies focus heavily on ASD contexts, using visual behavioural features for facial emotion recognition, body movement analysis, and engagement assessment.
- Key limitations include camera dependence, underused physiological sensors, poor reporting of robot expressiveness and control, and limited explainability and ethical consideration.
- An integrative taxonomy of six dimensions advocates multimodal, embodied, developmentally appropriate, explainable, and ethically grounded affective CRI beyond automatic emotion recognition.
Sensors (Basel). 2026 Sep 9;26(18):5721. doi: 10.3390/s26185721.
ABSTRACT
Affective computing has become increasingly relevant in child-robot interaction (CRI), particularly in social robotics, emotion recognition, engagement assessment, and autism-related interventions. This systematic review with a critical and integrative synthesis analyzes 105 included studies to examine how affect is sensed, represented, processed, expressed, and evaluated in CRI. The literature search was conducted in IEEE Xplore, Web of Science, Scopus, and PubMed, following a systematic screening process guided by the review objectives. A descriptive and structured narrative synthesis was conducted considering publication characteristics, robot platform and morphology, target population, sensing modalities and observed affect-relevant features, affective constructs and representation models, computational and control mechanisms, robot affective expression, evaluation strategies, and remaining research gaps. The findings show a strong emphasis on ASD-related contexts, visually observable and behavioral features, facial emotion recognition, body movement analysis, and engagement assessment. The review also identifies important limitations, including reliance on camera-based affect recognition, comparatively limited use of physiological and other complementary sensing modalities, unclear alignment between robot roles and interaction strategies, insufficient reporting of robot emotional expressiveness and control mechanisms, and limited attention to explainability, data governance, and long-term ethical implications. Based on these findings, an integrative taxonomy of affective computing in CRI is proposed, comprising six interconnected dimensions: interaction context; sensing modalities and observed features; affective constructs and representation models; computational and control mechanisms; robot affective expression; and evaluation and adaptation strategies. Rather than treating these dimensions as entirely novel categories, the taxonomy consolidates and extends previously fragmented classifications into a child-centered representation of the affective interaction process. Overall, this review argues that affective CRI should move beyond automatic emotion recognition toward multimodal, embodied, developmentally appropriate, explainable, and ethically grounded robot interaction.
PMID:42817270 | DOI:10.3390/s26185721
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