반려동물 IoT 디바이스용 심박 및 호흡수 측정 신호처리

초록

Conventional frequency-domain approaches for estimating the heart rate (HR) and respiratory rate (RR) of companion animals are computationally demanding, making them unsuitable for low-power embedded devices. They also suffer from limited real-time applicability and vulnerability to motion artifacts. To address these challenges, this study proposes a novel time-domain signal processing method for estimating HR and RR in companion animals. The proposed method consists of motion detection and gating, heartbeat detection, and the elimination of falsely detected HR and RR values. When applied to dogs, the proposed approach achieved mean errors of 3.0 bpm for HR and 1.7 bpm for RR. These results demonstrate that the method can be implemented in real time on low-power MCU-based wearable devices, highlighting its potential for daily health monitoring of companion animals as well as for use as an auxiliary tool in veterinary diagnostics.

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