AcHand: Detecting Respiratory Rate of Operator Via Smartphone Microphone

In this article, we dedicate a handgrip-free technique, called “AcHand,” to continuously detect respiratory rates under acoustic signals sensing via a smartphone. Although the particularity of the AcHand is to follow the subtle movements of the heart during its displacement, we acknowledge specific challenges that can make the signal vulnerable to handgrip-free scenarios such as: 1) stochastic hand activities: fluctuations in movement can alter the trajectory of the acoustic signal to the microphones and cause random changes in the breathing curve, resulting in complex and unpredictable dynamics and 2) clicking on the phone screen can disrupt the breathing by generating a large signal variation to create undesirable peaks to noisy the signal. However, by analyzing the recorded signal reflection evoked by a chirp sound stimulus, we propose a linear transformation of the cardioreflector–manoreflector to analyze the respiratory patterns variation and distinguish weak breathing signals under the handgrip-free scenario. We compare the present signal with the ground truth recorded by ECG to approve the efficiency of the proposed method. Our experiments show that AcHand achieves a mean absolute error (MAE) of 0.341 bpm, which is a significant improvement over the state-of-the-art devices, and enhancing detection capability across handgrip-free scenarios.