Abstract
Healthcare professionals work in complex, high-stakes environments where effective communication is critical for care delivery, team coordination, and individual well-being. However, communication activity in everyday clinical settings remains challenging to measure and largely unexplored in human behavioral research. We present VoxCare, a scalable egocentric wearable audio sensing and computing system that captures natural communication behaviors of hospital professionals in real-world settings without storing raw audio. VoxCare performs real-time, on-device acoustic feature extraction and applies a speech foundation model-guided teacher-student framework to identify foreground speech activity. From these features, VoxCare derives interpretable behavioral measures of communication frequency, duration, and vocal arousal. Our analyses reveal how, when, and how often clinicians communicate across different shifts and working units, and suggest that communication activity reflects underlying workload and stress. By enabling continuous assessment of communication patterns in everyday contexts, this study provides data-driven approaches to understand the behaviors of healthcare providers and ultimately improve healthcare delivery.
Abstract (translated)
医疗专业人员在复杂且高风险的环境中工作，有效沟通对于护理提供、团队协作和个人福祉至关重要。然而，在日常临床环境中的沟通活动仍然难以量化和研究，特别是在人类行为学领域。本文介绍了VoxCare系统，这是一个可扩展的自为中心式穿戴音频感知与计算系统，能够在不存储原始音频的情况下捕捉医院专业人员在现实世界环境中自然发生的沟通行为。VoxCare能够实时、在设备上进行声学特征提取，并采用以语音基础模型指导的教师-学生框架来识别前景中的言语活动。从这些特征中，VoxCare推导出可解释的行为指标，包括沟通频率、持续时间和语调的紧张程度。 我们的分析揭示了临床医生在不同班次和工作单位中何时以及如何频繁地进行沟通，并表明沟通行为反映了潜在的工作量和压力水平。通过连续评估日常环境中医疗保健提供者的沟通模式，这项研究为理解其行为提供了基于数据的方法，并最终旨在改善医疗服务的质量。
URL
https://arxiv.org/abs/2603.10888
