Improved sensor selection method during movement for breathing rate estimation with unobtrusive pressure sensor arrays

Gilakjani, S. Soleimani; Azimi, Hilda; Bouchard, M.; Goubran, Rafik; Knoefel, Frank

doi:10.1109/sas.2018.8336769

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…Other researchers have focused on the detection of the human pulses or respiration [22][23][24][25][26][27] as it is inconvenient to check patients' characteristics in their sleep breathing and pulse measurement experiments by using electrodes and straps. Mora et al [25] proposed an unobtrusive sleep monitoring system for the detection of sleep apnea-hypopnea syndrome (SAHS).…”

Section: Related Workmentioning

confidence: 99%

Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

Liu¹,

Wang²,

She³

et al. 2021

Computers, Materials &Amp; Continua

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An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data. Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model, determining automatically the intention of the user to open or close the mosquito net. We used optical flow to extract pressure map features, and they were fed to a 3-dimensional convolutional neural network (3D-CNN) classification model subsequently. We achieved the expected results using a nested cross-validation method to evaluate our model. Deep learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of users. This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

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Section: Related Workmentioning

confidence: 99%

Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

Liu¹,

Wang²,

She³

et al. 2021

Computers, Materials &Amp; Continua

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“…They are Pearson's correlation coefficient (PCC)-based signal fusion and signalto-noise ratio (SNR)-MAX-based signal fusion. Later in [20], for more complex data containing movement in bed and different types of breathing, such as shallow breathing, deep breathing, and periods of apnea, the SNR-MAX was found to be the best method of sensor signal combining, resulting in the highest Pearson Correlation Coefficient with the respiratory band signal as the gold standard. Therefore, in this section, the SNR-MAX method is applied to the 72 sensor signals to generate a single output signal with better signal quality.…”

Section: B Preprocessingmentioning

confidence: 99%

Unobtrusive Screening of Central Sleep Apnea From Pressure Sensors Measurements: A Patient-Specific Longitudinal Study

Azimi

Bouchard

Goubran

et al. 2020

IEEE Trans. Instrum. Meas.

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Historically, the lack of patients' sleep histories has caused low identification of sleep apnea (SA) and referral rates. Moreover, the costly and time-consuming nature of polysomnography (PSG) as a standard clinical test for detecting SA and the lack of sleep clinics has created a demand for suitable home-based monitoring devices. Pressure measurement using a pressure sensitive mat (PSM) can address the challenges found in current sleep-monitoring solutions. The noncontact PSM has a potential to replace obtrusive breathing sensors in the sleep lab and to be used as a prescreening tool for patients suspected of having SA. Applying classical support vector machine (SVM), this article presents a personalized system based on the measurements of each patient to detect central SA (CSA) events and monitor sleep characteristics longitudinally. For this purpose, sensor set-ups were installed in nine seniors' homes to collect unsupervised pressure data in approximately one year ranging from 8 to 12 months. Cost-based and resampling-based approaches were examined to combat imbalanced data. The results showed that the cost-based method outperformed other methods. Next, the patient-specific system was used to determine the total number of CSA events, as well as their starting time and duration in each day. The SA severity was measured by the central apnea index (CAI). In addition, other sleep characteristics such as bed occupancy (BO), day clock, and night clock were extracted from the PSM measurements. The impact of longitudinal sleep monitoring could be in tracking SA treatment progression, and possibly providing information on the interaction between SA and other disease progressions.

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“…Although existing works deal with sensor selection [18] or sensor placement [19] problems, to the best of our knowledge, no effort was done to investigate the contribution of sensors based on a game theory paradigm widely used in machine learning explainability [20], [21], [22], [23], called Shapley value [24]. Therefore, in this paper, we propose an approach to interpret the importance of each sensor in BSE based on the Shapley value.…”

Section: Introductionmentioning

confidence: 99%