Swallow monitoring through apnea detection in breathing signal

Dong, Bo; Biswas, Subir

doi:10.1109/embc.2012.6347444

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Several wearable techniques are proposed to detect food or liquid intake including inertial measurements of the wrist or body [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ], or textile-based measurements [ 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ]. The majority of studies analyzed and classified food intake with less focus on liquid intake.…”

Section: Wearable Technologymentioning

confidence: 99%

“…The accuracy of the system was given by the area under the receiver operating characteristic (ROC) curve, and was greater than 0.9 when tested on 14 subjects [ 80 ]. Dong and Biswas expanded this idea with a chest piezo respiration belt to detect pauses in breathing that occur when one is swallowing [ 62 , 81 , 82 , 83 , 84 , 85 ]. This belt had no contact with the skin and converted tension from chest motion into respiration signals.…”

Section: Wearable Technologymentioning

confidence: 99%

“…This belt had no contact with the skin and converted tension from chest motion into respiration signals. They first used template matching to classify normal breathing, swallowing while exhaling and swallowing while inhaling [ 81 ]. This template-matching algorithm was tested only on three subjects with their own customized templates.…”

Section: Wearable Technologymentioning

confidence: 99%

See 2 more Smart Citations

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

2021

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Fluid intake monitoring is an essential component in preventing dehydration and overhydration, especially for the senior population. Numerous critical health problems are associated with poor or excessive drinking such as swelling of the brain and heart failure. Real-time systems for monitoring fluid intake will not only measure the exact amount consumed by the users, but could also motivate people to maintain a healthy lifestyle by providing feedback to encourage them to hydrate regularly throughout the day. This paper reviews the most recent solutions to automatic fluid intake monitoring both commercially and in the literature. The available technologies are divided into four categories: wearables, surfaces with embedded sensors, vision- and environmental-based solutions, and smart containers. A detailed performance evaluation was carried out considering detection accuracy, usability and availability. It was observed that the most promising results came from studies that used data fusion from multiple technologies, compared to using an individual technology. The areas that need further research and the challenges for each category are discussed in detail.

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Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

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Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

2021

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“…Lazareck et al [18] [17] proposed a noninvasive, acoustic-based method to differentiate between individuals with and without dysphagia. Wearable monitoring swallow system was developed [6] in order to assess an overall food and drink intake habits as well as other swallow related disorders. For example, piezo-respiratory belt able to be used for identifying non-intake swallows, solid intake swallows, and drinking swallows is in development.…”

Section: State Of the Artmentioning

confidence: 99%

Swallowing Sound Recognition at Home Using GMM

Khlaifi

Istrate

Demongeot

et al. 2018

IRBM

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Aiming for autonomous living for the people after a stroke is the challenge these days especially for swallowing disorders or dysphagia. It is in this context that the e-swallhome project proposes to develop tools, from hospital care until the patient returns home, which are able to monitor in real time the process of swallowing. This paper proposes a non-invasive acoustic based method to differentiate between swallowing sounds and other sounds in normal ambient environment during food intake. Gaussian Mixture Models (GMM) adapted through the Expectation Maximization (EM) algorithm was employed for classification and test combination using the leave-one-out approach according to the small amount of data in our database. Results provide a good recognition rate of 100 %.

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Analyzing Breathing Signals and Swallow Sequence Locality for Solid Food Intake Monitoring

Dong

Biswas

2016

J. Med. Biol. Eng.

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Self-reported questionnaires are widely used by researchers for analyzing the dietary behavior of overweight and obese individuals. It has been established that questionnaire-based data collection often suffers from high errors due to its reporting subjectivity. Automatic swallow detection, as an alternative to questionnaires, is proposed in this paper to avoid such subjectivity. Existing approaches for swallow detection include the use of surface electromyography and sound to detect individual swallowing events. Many of these methods are generally too complicated and cumbersome for daily usage in a free-living setting. This paper presents a wearable solid food intake monitoring system that analyzes human breathing signals and swallow sequence locality. Food intake is identified by detecting swallow events. The system works based on a key observation that the otherwise continuous breathing process is interrupted by a short apnea during swallowing. A support vector machine (SVM) is first used for detecting such apneas in breathing signals collected from a wearable chest belt. The resulting swallow detection is then refined using a hidden Markov model (HMM)-based mechanism that leverages the known temporal locality in the sequence of human swallows. Temporal locality is based on the fact that people usually do not swallow in consecutive breathing cycles. The HMM model is used to model such temporal locality in order to refine the SVM results. Experiments were carried out on six healthy subjects wearing the proposed system. The proposed SVM method achieved up to 61% precision and 91% recall on average. Utilization of HMM in addition to SVM improved the overall performance to up to 75% precision and 86% recall.

show abstract

Swallow monitoring through apnea detection in breathing signal

Cited by 8 publications

References 7 publications

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

Swallowing Sound Recognition at Home Using GMM

Analyzing Breathing Signals and Swallow Sequence Locality for Solid Food Intake Monitoring

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