Respiratory rate (RR) is a key vital sign for clinical assessment and mental well-being, yet it is rarely monitored in everyday life due to the lack of unobtrusive sensing technologies. In-ear audio sensing is promising due to its high social acceptance and the amplification of physiological sounds caused by the occlusion effect; however, existing approaches often fail under real-world noise or rely on computationally expensive models. We present EarResp-ANS, the first system enabling fully on-device, real-time RR estimation on commercial earphones. The system employs LMS-based adaptive noise suppression (ANS) to attenuate ambient noise while preserving respiration-related acoustic components, without requiring neural networks or audio streaming, thereby explicitly addressing the energy and privacy constraints of wearable devices. We evaluate EarResp-ANS in a study with 18 participants under realistic acoustic conditions, including music, cafeteria noise, and white noise up to 80 dB SPL. EarResp-ANS achieves robust performance with a global MAE of 0.84 CPM , reduced to 0.47 CPM via automatic outlier rejection, while operating with less than 2% processor load directly on the earphone.

翻译：呼吸率是临床评估和心理健康监测的关键生命体征，但由于缺乏非侵入式传感技术，其在日常生活中的监测很少。耳内音频传感因其较高的社会接受度以及由闭塞效应引起的生理声音放大而前景广阔；然而，现有方法在真实世界噪声下常常失效，或依赖于计算成本高昂的模型。我们提出了EarResp-ANS，这是首个能够在商用耳机上实现完全端侧、实时呼吸率估计的系统。该系统采用基于LMS的自适应噪声抑制技术，在保留与呼吸相关的声学成分的同时衰减环境噪声，无需神经网络或音频流传输，从而明确应对了可穿戴设备的能耗和隐私限制。我们在包含18名参与者的研究中，在包括音乐、餐厅噪声和白噪声（高达80 dB SPL）的真实声学条件下评估了EarResp-ANS。EarResp-ANS实现了稳健的性能，全局平均绝对误差为0.84 CPM，通过自动异常值剔除可降至0.47 CPM，同时在耳机上直接运行的处理器负载低于2%。