In-Ear Audio Wearable: Measurement of Heart and Breathing Rates for Health and Safety Monitoring

Martin, Alexis; Voix, Jérémie

doi:10.1109/tbme.2017.2720463

Cited by 70 publications

(57 citation statements)

References 15 publications

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“…This contrasts with the environmental category since radars are used in more than 33% of the studies, being the leading technology followed by cameras (18%) and fiber optic sensors (14%). There are types of sensors, such as magnetometers, gyroscopes, microphones, optical sensors, inductive sensors, or thermistors, in which its use is very limited in both categories [ 62 , 73 , 76 , 77 , 81 , 91 , 95 , 106 , 107 , 115 , 119 , 120 , 131 , 135 , 156 , 160 , 162 , 185 , 193 , 217 ].…”

Section: Resultsmentioning

confidence: 99%

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

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Section: Resultsmentioning

confidence: 99%

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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“…Non-speech audio signals are recorded as described by [3]. In each ear, participants were equipped with an intra-aural device developed by EERS Global Technologies Inc. (Montreal, Canada), shown in Fig.…”

Section: Methodology 21 Data Collectionmentioning

confidence: 99%

“…Therefore, a noisy dataset was created post-hoc by adding factory noise from the NOISEX-92 database [21] at 10 dB signal-to-noise ratio (SNR). In situations where the SNR is much lower, denoising algorithms would be used to clean the captured in-ear signals [3,22]. For the scope of this paper, we will look at signals degraded by noise but not to the extent where denoising algorithms are engaged.…”

Section: Methodology 21 Data Collectionmentioning

confidence: 99%

“…Capturing and classifying such nonverbal events has gained much interest over the years [1,2]. Namely, the combination of well performing machine learning algorithms and the boom in wearable devices has opened up a door for continuous health monitoring [3,4]. Often, these nonverbal signals are either inaudible (blinking), too faint (clearing the throat), or overlooked (teeth clicking) when considering the communication signals sent by the human body.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, by way of the occlusion effect, intra-aural devices that create an acoustical seal in the ear canal have access to an extensive variety of human produced verbal and nonverbal audio events. Martin and Voix [3], have shown that the occluded ear is a reliable place to capture breathing and heartbeat signals for health monitoring. However, other relevant signals such as blinking, coughing and clicking of the teeth can also be recorded from inside the occluded ear.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Nonverbal Human Produced Audio Events: A Pilot Study

et al. 2018

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The accurate classification of nonverbal human produced audio events opens the door to numerous applications beyond health monitoring. Voluntary events, such as tongue clicking and teeth chattering, may lead to a novel way of silent interface command. Involuntary events, such as coughing and clearing the throat, may advance the current state-of-the-art in hearing health research. The challenge of such applications is the balance between the processing capabilities of a small intra-aural device and the accuracy of classification. In this pilot study, 10 nonverbal audio events are captured inside the ear canal blocked by an intra-aural device. The performance of three classifiers is investigated: Gaussian Mixture Model (GMM), Support Vector Machine and Multi-Layer Perceptron. Each classifier is trained using three different feature vector structures constructed using the mel-frequency cepstral (MFCC) coefficients and their derivatives. Fusion of the MFCCs with the auditoryinspired amplitude modulation features (AAMF) is also investigated. Classification is compared between binaural and monaural training sets as well as for noisy and clean conditions. The highest accuracy is achieved at 75.45% using the GMM classifier with the binaural MFCC+AAMF clean training set. Accuracy of 73.47% is achieved by training and testing the classifier with the binaural clean and noisy dataset.

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