ABSTRACT
Wearing face masks (alongside physical distancing) provides some protection against COVID-19. Face masks can also change how people communicate and subsequently affect speech signal quality. This study investigated how two common face mask types affect acoustic analysis of speech perception. Quantitative and qualitative assessments were carried out in terms of measuring the sound pressure levels and playing back to a group of people. The responses gauged proved that masks alter the speech signal with downstream effects on speech intelligibility of a speaker. Masks muffle speech sounds at higher frequencies and hence the acoustic effect of a speaker wearing a face mask is equivalent to the listener having slight high frequency hearing loss. When asked on the perception of audibility, over 83% of the participants were able to clearly hear the no mask audio clip, however, 41% of the participants thought it was moderately audible with N95 and face shield masks. Due to no visual access, face masks act as communication barriers with 50% of the people finding to understand people because they could not read their lips. Nevertheless, based on these findings it is reasonable to hypothesize that wearing a mask would attenuate speech spectra at similar frequency bands. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.
ABSTRACT
Owing to the Covid-19 epidemic, medical radar has become a potential non-contact method in patient monitoring. However, this radar type is sensitive to external interference. The output signal obtained by the radar when a patient makes random body movements can significantly reduce the accuracy of vital sign detection algorithms. In addition, algorithms should be developed for actual application. In this study, we present an improved model of the 24-GHz radar signal quality classification system and a technique to enhance the resolution of respiration rate (RR) and heart rate (HR) for short time interval signals. Moreover, a complete system including signal quality assessment and vital signs extraction is implemented in real time on the Lab-VIEW software. The signal quality classification was evaluated on the measured signals of 10 healthy subjects. Accordingly, the obtained results indicate that with specific features, the accuracy of signal quality classification reaches 89.8%-100% while real-time RR and HR extraction results demonstrate significant agreement between radar measurement and the contact-type sensor. © 2022 IEEE.
ABSTRACT
Monitoring respiration using mobile technology has potential to contribute to the clinical management of patients with infectious diseases or chronic respiratory conditions in the home. In this study, a new method to estimate respiratory rate and exhale duration from audio data recorded using smartphone microphones was developed. The method first determines the fundamental frequency of the audio signal, which guides an adaptive thresholding method to detect individual exhales. Exhale boundary times were refined using adaptive physiological thresholds. To control for environmental noise in remote audio recordings, a method to classify audio signals as acceptable or unacceptable for accurate respiration monitoring was developed. Estimated respiratory rates and audio exhale durations were validated against respiratory inductance plethysmography (RIP) in 27 healthy participants. A further 217 audio recordings were collected remotely by 210 healthy and COVID-19 participants, with results compared with researcher annotations. Compared to RIP in the laboratory, respiratory rate was estimated with a mean absolute error (MAE) of 0.2 ± 0.27 bpm, and within 1 bpm for 96 % of recordings (r = 0.99). Compared to researcher annotations for remote recordings, respiratory rate was estimated with a MAE of 0.79 ± 2.44 bpm, and within 1 bpm for 87.5 % of recordings (r = 0.92), while audio exhale duration was estimated with a MAE of 0.21 ± 0.23 s. Audio signal quality was classified with an area under the receiver operating characteristic of 0.81 ±. This method offers the potential for accurate remote monitoring of respiratory rate and breathing patterns in individuals with COVID-19 and other chronic respiratory conditions.
ABSTRACT
Pulse oximeters are now a part of every household first-aid kit, pulse oximeters have actually helped to primarily identify the severity of covid19 infection in a person's body. These devices measure the saturated blood oxygen level (SpO2) in a person's body, there by the displayed level of SpO2 helps medical professionals to hypothesize the situation and provide a better aid for the patient. Since the process is non-invasive, the devices are widely implemented. Pulse oximeters acquire photoplethysmographic (PPG) signals, these signals contain the volumetric changes in human blood, that on being exposed to mathematical principles give the SpO2 reading and other data. The process of obtaining the PPG signals through pulse oximetry employs a mechanism of emitting and detecting the IR and Red signals through human tissues, however during the capturing of reflected signals through detector, the detected signal comes along with noise referred as motion artifact (MA). These MAs arises due to the voluntary/involuntary movements of human causing volumetric changes in flow of blood at the source and detector sensor locations. The presence of MAs in such signals turns up to erroneous SpO2 level estimation, that creates a problem for medical professionals in treating the diseases. To improve the reliability of SpO2 estimation, by a pulse oximeter, the PPG signal quality is to be enhanced. In this paper, the authors tried to describe on the work of enhancing the acquired PPG signal quality by reducing MAs with effective methods. © 2022 IEEE.
ABSTRACT
Purpose>Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.Design/methodology/approach>This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.Findings>The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.Originality/value>This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.
ABSTRACT
Mobile broadband (MBB) is one of the critical goals in fifth-generation (5G) networks due to rising data demand. MBB provides very high-speed internet access with seamless connections. Existing MBB, including third-generation (3G) and fourth-generation (4G) networks, also requires monitoring to ensure good network performance. Thus, performing analysis of existing MBB assists mobile network operators (MNOs) in further improving their MBB networks’ capabilities to meet user satisfaction. In this paper, we analyzed and evaluated the multidimensional performance of existing MBB in Oman. Drive test measurements were carried out in four urban and suburban cities: Muscat, Ibra, Sur and Bahla. This study aimed to analyze and understand the MBB performance, but it did not benchmark the performance of MNOs. The data measurements were collected through drive tests from two MNOs supporting 3G and 4G technologies: Omantel and Ooredoo. Several performance metrics were measured during the drive tests, such as signal quality, throughput (downlink and unlink), ping and handover. The measurement results demonstrate that 4G technologies were the dominant networks in most of the tested cities during the drive test. The average downlink and uplink data rates were 18 Mbps and 13 Mbps, respectively, whereas the average ping and pong loss were 53 ms and 0.9, respectively, for all MNOs.