用耳机实时测呼吸率，降噪强且省电。
EarResp-ANS : Audio-Based On-Device Respiration Rate Estimation on Earphones with Adaptive Noise Suppression
- 基于自适应噪声抑制技术，在耳塞内直接降噪
- 误差仅0.84次/分钟，剔除异常值后降至0.47
- 全程本地运行，处理器负载低于2%
呼吸率（RR）是临床评估和心理健康的關鍵生命體徵，但日常生活中因缺乏無感感知技術而難以監測。入耳式音頻感知因其高社會接受度及閉塞效應帶來的生理聲音增強而具有前景，但現有方法在真實環境噪音下表現不佳或依賴計算開銷大的模型。本文提出 EarResp-ANS，首個實現商用耳機上全本地、實時呼吸率估計的系統。該系統採用基於LMS的自適應降噪（ANS）技術，在不使用神經網絡或音頻流傳輸的前提下，有效抑制環境噪音並保留與呼吸相關的聲學成分，明確解決可穿戴設備的能耗與隱私限制。我們在18名受試者上進行實驗，測試條件包含音樂、餐廳噪音及最高達80 dB SPL的白噪音。結果顯示，系統全局平均絕對誤差（MAE）為0.84次/分鐘，經自動異常值剔除後降低至0.47次/分鐘，且在耳機端運行時處理器負載低於2%。
原文摘要 · Abstract (English)
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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