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The quality of parent-child interactions at an early age has been linked to children's social-emotional development, executive function, and risk for behavior problems. As such, parent-child interactions in naturalistic settings could present a unique opportunity to screen for at-risk behavior in young children, enabling timely and targeted interventions. In this work, we validate the feasibility of using structured at-home play sessions, completed via the Tandem smartphone app, to enable highly accurate and scalable behavioral assessments. We demonstrate that audio and physiological signals recorded during the play session can be used to capture key markers of parent-child interaction dynamics, which are more indicative of at-risk behavior compared to features from each individual alone. We propose novel audio-based dyadic interaction features that significantly outperform conventional speech features at predicting risk for behavior problems, achieving an F1 score of 0.87. Furthermore, we show that dyadic physiological synchrony features, extracted from privacy-preserving wearable sensor data, can classify at-risk behavior with an F1 score of 0.91. Tandem thus sets the stage for automated at-home behavior assessment tools for young children that balance screening accuracy with practical deployment considerations. 

ABSTRACT The electrode–skin interface presents a fundamental challenge in bioelectronics: maintaining stable electrical contact with tissue that undergoes constant physiological changes. Here we demonstrate a paradigm shift by engineering a self‐sustaining microenvironment at the skin–electrode interface through the integration of Au‐coated fabric electrodes with a humidity‐regulating cooling patch. Our smart electrode‐integrated cooling (SEIC) patch uses evaporative cooling to create a humid microenvironment where water condensation continuously regenerates ionic pathways between skin and electrode. This transforms the interface from a static contact point to a dynamic, self‐renewing electrochemical junction. The SEIC patch exhibits 200‐fold and 10‐fold lower impedances than Au‐coated fabric electrodes and conventional electrodes, respectively; maintains performance through 2000 attachment cycles; enables stable biosignal acquisition for eight days; and preserves signal fidelity under motion. Moreover, the SEIC demonstrates applications in continuous cardiac monitoring during daily life, and real‐time 3D facial animation reconstruction in virtual reality via high‐fidelity capture of facial muscle activity. This work opens new frontiers in preventive medicine and human–computer interaction. 

There has been growing interest in developing ubiquitous technologies to analyze adult-child speech in naturalistic settings such as free play in order to support children's social and academic development, language acquisition, and parent-child interactions. However, these technologies often rely on off-the-shelf speech processing tools that have not been evaluated on child speech or child-directed adult speech, whose unique characteristics might result in significant performance gaps when using models trained on adult speech. This work introduces the Playlogue dataset containing over 33 hours of long-form, naturalistic, play-based adult-child conversations from three different corpora of preschool-aged children. Playlogue enables researchers to train and evaluate speaker diarization and automatic speech recognition models on child-centered speech. We demonstrate the lack of generalizability of existing state-of-the-art models when evaluated on Playlogue, and show how fine-tuning models on adult-child speech mitigates the performance gap to some extent but still leaves considerable room for improvement. We further annotate over 5 hours of the Playlogue dataset with 8668 validated adult and child speech act labels, which can be used to train and evaluate models to provide clinically relevant feedback on parent-child interactions. We investigate the performance of state-of-the-art language models at automatically predicting these speech act labels, achieving significant accuracy with simple chain-of-thought prompting or minimal fine-tuning. We use inhome pilot data to validate the generalizability of models trained on Playlogue, demonstrating its utility in improving speech and language technologies for child-centered conversations. The Playlogue dataset is available for download at https://huggingface.co/datasets/playlogue/playlogue-v1. 

Wireless sensing has demonstrated its potential of utilizing radio frequency (RF) signals to sense individuals and objects. Among different wireless signals, LoRa signal is particularly promising for through-wall sensing owing to its strong penetration capability. However, existing works view walls as a bad thing as they attenuate signal power and decrease the sensing coverage. In this paper, we show a counter-intuitive observation, i.e., walls can be used to increase the sensing coverage if the RF devices are placed properly with respect to walls. To fully understand the underlying principle behind this observation, we develop a through-wall sensing model to mathematically quantify the effect of walls. We further show that besides increasing the sensing coverage, we can also use the wall to help mitigate interference, which is one well-known issue in wireless sensing. We demonstrate the effect of wall through two representative applications, i.e., macro-level human walking sensing and micro-level human respiration monitoring. Comprehensive experiments show that by properly deploying the transmitter and receiver with respect to the wall, the coverage of human walking detection can be expanded by more than 160%. By leveraging the effect of wall to mitigate interference, we can sense the tiny respiration of target even in the presence of three interferers walking nearby.