A Buffered Human – AI Interaction Model for Cognitive Offloading in Infant Care Using Explainable Multimodal Context Sensing
Nighttime infant crying frequently forces sleep - deprived caregivers to make hasty and error - prone decisions. Conventional baby monitoring devices usually respond to detected crying with loud auditory alarms. This practice may increase stress levels without providing practical guidance. This paper introduces an intelligent parenting assistant that integrates audio, visual, and environmental/contextual sensing to infer the probable causes of crying and implement a buffered response sequence. The proposed framework combines acoustic cry patterns, infant motion data, and routine/contextual cues to generate an interpretable inference about potential causes. Upon detecting crying, the system first initiates low - arousal soothing interventions, such as white noise or gentle rocking, while briefly delaying the notification to the caregiver. After this buffer period, a mobile application presents the primary cause hypothesis along with a confidence estimate and supporting evidence in plain language, allowing the caregiver to verify, override, or take control. Evaluations using simulated nighttime care scenarios and expert heuristic assessment show that the assistant reduced the average response time from approximately 17 to 9 minutes, shortened crying episodes, and was associated with lower self reported caregiver anxiety. These results suggest that explainable, multimodal AI can advance infant monitoring from simple alert systems to collaborative caregiving support.
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A Buffered Human – AI Interaction Model for Cognitive Offloading in Infant Care Using Explainable Multimodal Context Sensing
Semantic Scholar · 2026
Abstract
Nighttime infant crying frequently forces sleep - deprived caregivers to make hasty and error - prone decisions. Conventional baby monitoring devices usually respond to detected crying with loud auditory alarms. This practice may increase stress levels without providing practical guidance. This paper introduces an intelligent parenting assistant that integrates audio, visual, and environmental/contextual sensing to infer the probable causes of crying and implement a buffered response sequence. The proposed framework combines acoustic cry patterns, infant motion data, and routine/contextual cues to generate an interpretable inference about potential causes. Upon detecting crying, the system first initiates low - arousal soothing interventions, such as white noise or gentle rocking, while briefly delaying the notification to the caregiver. After this buffer period, a mobile application presents the primary cause hypothesis along with a confidence estimate and supporting evidence in plain language, allowing the caregiver to verify, override, or take control. Evaluations using simulated nighttime care scenarios and expert heuristic assessment show that the assistant reduced the average response time from approximately 17 to 9 minutes, shortened crying episodes, and was associated with lower self reported caregiver anxiety. These results suggest that explainable, multimodal AI can advance infant monitoring from simple alert systems to collaborative caregiving support.