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— Stethoscopes are commonly used to diagnose cardiac and respiratory diseases. The advent of digital stethoscopes added the capability to remotely monitor the cardiorespiratory well-being of patients and facilitate digital remote auscultations for physicians to examine their patients outside of the clinic. To increase the adoption of a hybrid diagnosis between in-person and remote auscultations, it is important to characterize the frequency response of different configurations of digital stethoscopes. A standard testing platform was adopted from the literature and re-validated by seven physicians. Two wearable and two non-wearable digital stethoscopes were investigated. A similarity factor score was used to analyze select time-synchronized acoustic events that achieved high fidelity between stethoscopes. The frequency responses between devices were compared using the correlation coefficient. Two devices reported the highest correlation coefficient of 0.72 and 0.75 in the wearable and non-wearable categories, respectively. The correlation coefficient of the frequency responses between the same pair of devices on the human chest was 0.91. With such a high agreement in the frequency response between the wearable and non-wearable digital stethoscope, it can be concluded that the configuration of digital stethoscope is not a limiting factor of performance, and both are acceptable and desirable for physicians to adopt either configuration in remote delivery of healthcare.

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm’s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.

Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Donning face masks could reduce the risk of viral transmissions and is currently practiced in medical facilities. An acoustic-sensing device was attached to face masks to assist medical facilities in remotely monitoring patients’ respiration rate and wheeze occurrence. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants. Participants were blindfolded throughout the data collection process. Respiratory rates of the participants were evaluated for one minute. The wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. No discomfort was reported from the participants who used the modified mask. The mean error of respiratory rate was found to be 2.0 ± 1.3 breath per minute. The overall accuracy of the wheeze detection algorithm was 91.9%. The microphone sensor that was first designed to be chest-worn has been proven versatile to be adopted as a mask attachment. The current findings support and suggest the use of the proposed mask attachment in medical facilities. This application can be especially helpful in managing a sudden influx of patients in the face of a pandemic.