Automatic detection and classification of acoustic breathing cycles

Yahya, Omar; Faezipour, Miad

doi:10.1109/aseezone1.2014.6820648

Cited by 20 publications

(9 citation statements)

References 18 publications

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“…Minimal variations in the acquisition process resulted from the dependability of angles of recording on subjects’ preferred UM positioning in relation to the UM’s diaphragm. Breathing intensity at the input of the microphone was hence slightly variant among the subjects [ 8 ]. However, variations were minimal between the training and live datasets of the same subject, with limited effects on classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…The detection of human breathing can be accomplished through different modalities that expand to comprise a wide range of techniques. Common collection methods include the usage of electroencephalogram (EEG) signals [ 8 ], smart wearable garments with fiber optic sensors [ 9 ], photoplethysmogram measurements of cardiac activity [ 10 ], pressure and thermal sensors [ 11 ], and airflow examination [ 8 ]. The utilization of acoustic signals and microphones to capture breathing signals is common; however this is for clinical respiratory measurement purposes [ 8 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Breathing Pattern Interpretation as an Alternative and Effective Voice Communication Solution

et al. 2018

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Augmentative and alternative communication (AAC) systems tend to rely on the interpretation of purposeful gestures for interaction. Existing AAC methods could be cumbersome and limit the solutions in terms of versatility. The study aims to interpret breathing patterns (BPs) to converse with the outside world by means of a unidirectional microphone and researches breathing-pattern interpretation (BPI) to encode messages in an interactive manner with minimal training. We present BP processing work with (1) output synthesized machine-spoken words (SMSW) along with single-channel Weiner filtering (WF) for signal de-noising, and (2) k-nearest neighbor (k-NN) classification of BPs associated with embedded dynamic time warping (DTW). An approved protocol to collect analogue modulated BP sets belonging to 4 distinct classes with 10 training BPs per class and 5 live BPs per class was implemented with 23 healthy subjects. An 86% accuracy of k-NN classification was obtained with decreasing error rates of 17%, 14%, and 11% for the live classifications of classes 2, 3, and 4, respectively. The results express a systematic reliability of 89% with increased familiarity. The outcomes from the current AAC setup recommend a durable engineering solution directly beneficial to the sufferers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breathing Pattern Interpretation as an Alternative and Effective Voice Communication Solution

et al. 2018

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“…Yahya and colleagues [59] also detected respiration phases in audio data. Again, a VAD algorithm was applied to the audio signal to identify the respiration segments.…”

Section: Measuring Respiration From the Audio Signalmentioning

confidence: 97%

The role of respiration audio in multimodal analysis of movement qualities

Lussu

Niewiadomski

Volpe

et al. 2019

J Multimodal User Interfaces

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In this paper, we explore how the audio respiration signal can contribute to multimodal analysis of movement qualities. Within this aim, we propose two novel techniques which use the audio respiration signal captured by a standard microphone placed near to mouth and supervised machine learning algorithms. The first approach consists of the classification of a set of acoustic features extracted from exhalations of a person performing fluid or fragmented movements. In the second approach, the intrapersonal synchronization between the respiration and kinetic energy of body movements is used to distinguish the same qualities. First, the value of synchronization between modalities is computed using the Event Synchronization algorithm. Next, a set of features, computed from the value of synchronization, is used as an input to machine learning algorithms. Both approaches were applied to the multimodal corpus composed of short performances by three professionals performing fluid and fragmented movements. The total duration of the corpus is about 17 min. The highest F-score (0.87) for the first approach was obtained for the binary classification task using Support Vector Machines (SVM-LP). The best result for the same task using the second approach was obtained using Naive Bayes algorithm (F-score of 0.72). The results confirm that it is possible to infer information about the movement qualities from respiration audio.

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“…Next, they computed Mel-frequency cepstrum coefficients (MFCC) of respiration segments, and they used MFCC and a linear thresholding to distinguish between the two respiration phases. Yahya and colleagues [23] also classified respiration phases from audio data. Again, a VAD algorithm was applied to the audio signal to extract the respiration segments.…”

Section: State Of the Artmentioning

confidence: 99%

Using the Audio Respiration Signal for Multimodal Discrimination of Expressive Movement Qualities

Lussu

Niewiadomski

Volpe

et al. 2016

Human Behavior Understanding

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In this paper we propose a multimodal approach to distinguish between movements displaying three different expressive qualities: fluid, fragmented, and impulsive movements. Our approach is based on the Event Synchronization algorithm, which is applied to compute the amount of synchronization between two low-level features extracted from multimodal data. In more details, we use the energy of the audio respiration signal captured by a standard microphone placed near to the mouth, and the whole body kinetic energy estimated from motion capture data. The method was evaluated on 90 movement segments performed by 5 dancers. Results show that fragmented movements display higher average synchronization than fluid and impulsive movements.

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Automatic detection and classification of acoustic breathing cycles

Cited by 20 publications

References 18 publications

Breathing Pattern Interpretation as an Alternative and Effective Voice Communication Solution

Breathing Pattern Interpretation as an Alternative and Effective Voice Communication Solution

The role of respiration audio in multimodal analysis of movement qualities

Using the Audio Respiration Signal for Multimodal Discrimination of Expressive Movement Qualities

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