EarResp-ANS : 基于音频的设备端呼吸率估计在耳phones上具有自适应噪声抑制
EarResp-ANS : Audio-Based On-Device Respiration Rate Estimation on Earphones with Adaptive Noise Suppression
AI总结：
EarResp-ANS通过自适应噪声抑制技术，在耳phones上实现低功耗、实时呼吸率估计，无需神经网络或音频流，适用于日常环境中的无感监测。
摘要：
呼吸率（RR）是临床评估和心理健康的重要生命体征，但日常生活中很少被监测，因为缺乏无干扰的传感技术。在耳内的音频传感因其高社会接受度和耳道效应引起的生理声音放大而具有前景；然而，现有方法在现实世界噪声下往往失效或依赖于计算成本高的模型。我们提出了EarResp-ANS，这是首个能够在商用耳phones上实现完全设备端、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（ANS）来衰减环境噪声，同时保留与呼吸相关的声学成分，无需神经网络或音频流，从而明确解决了可穿戴设备的能量和隐私约束。我们在现实声学条件下对EarResp-ANS进行了研究，包括音乐、食堂噪音和高达80 dB SPL的白噪音。EarResp-ANS在18名参与者中实现了稳健的性能，全局MAE为0.84 CPM，通过自动异常值剔除后降至0.47 CPM，同时在耳phones上运行时处理器负载低于2%。
英文摘要：
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.
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