A Multi-Channel Ultrasound System for Non-Contact Heart Rate Monitoring

Ambrosanio, Michele; Franceschini, Stefano Sellari; Grassini, Giuseppe; Baselice, Fabio

doi:10.1109/jsen.2019.2949435

Cited by 27 publications

(17 citation statements)

References 57 publications

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“…of the active sonar signal and requires computationally combining both these components to compute R-R intervals. We build on prior work that uses ultrasonic devices 25,48 . These systems use custom hardware with ultrasound frequencies and sampling rates not supported by commodity smart speakers, transmit signals at a sound pressure level of 105 dBm at 30 cm 48 , which is about 300 times higher than that used by our prototype, achieve a limited range of 10-20 cm, and have not been clinically evaluated.…”

Section: Discussionmentioning

confidence: 99%

“…We build on prior work that uses ultrasonic devices 25 , 48 . These systems use custom hardware with ultrasound frequencies and sampling rates not supported by commodity smart speakers, transmit signals at a sound pressure level of 105 dBm at 30 cm 48 , which is about 300 times higher than that used by our prototype, achieve a limited range of 10–20 cm, and have not been clinically evaluated. Our system addresses these limitations and shows the feasibility of noncontact monitoring of individual heartbeats in both healthy and cardiac patients using smart speakers.…”

Section: Discussionmentioning

confidence: 99%

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Using smart speakers to contactlessly monitor heart rhythms

et al. 2021

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Heart rhythm assessment is indispensable in diagnosis and management of many cardiac conditions and to study heart rate variability in healthy individuals. We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner. Our algorithms transform the smart speaker into a short-range active sonar system and measure heart rate and inter-beat intervals (R-R intervals) for both regular and irregular rhythms. The smart speaker emits inaudible 18–22 kHz sound and receives echoes reflected from the human body that encode sub-mm displacements due to heart beats. We conducted a clinical study with both healthy participants and hospitalized cardiac patients with diverse structural and arrhythmic cardiac abnormalities including atrial fibrillation, flutter and congestive heart failure. Compared to electrocardiogram (ECG) data, our system computed R-R intervals for healthy participants with a median error of 28 ms over 12,280 heart beats and a correlation coefficient of 0.929. For hospitalized cardiac patients, the median error was 30 ms over 5639 heart beats with a correlation coefficient of 0.901. The increasing adoption of smart speakers in hospitals and homes may provide a means to realize the potential of our non-contact cardiac rhythm monitoring system for monitoring of contagious or quarantined patients, skin sensitive patients and in telemedicine settings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using smart speakers to contactlessly monitor heart rhythms

et al. 2021

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“…It has been shown that multi-channel ultrasonic detection systems have been widely used in medical and urban monitoring fields [ 34 , 35 ]. It will be a valuable research topic to build a multi-channel ultrasonic detection system and carry out data fusion to achieve concrete internal detection images.…”

Section: Discussionmentioning

confidence: 99%

Design and Performance Analysis of an Ultrasonic System for Health Monitoring of Concrete Structure

Zhao

Zheng

et al. 2021

Sensors

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The development and research of an ultrasonic-based concrete structural health monitoring system encounters a variety of problems, such as demands of decreasing complexity, high accuracy, and extendable system output. Aiming at these requirements, a low-cost extendable system based on FPGA with adjustable system output has been designed, and the performance has been evaluated by different assessment parameters set in this paper. Besides the description of the designed system and the experiments in air medium, the residual similarity and Pearson correlation coefficients of experimental and theoretical data have been used to evaluate the submodules’ output. The output performance of the overall system is evaluated by the Pearson correlation coefficient, root-mean-square error (RMSE), and magnitude-squared coherence with 40 experimental data. The maximum, median, minimum, and mean values in three-parameter datasets are analyzed for discussing the working condition of the system. The experimental results show that the system works stably and reliably with tunable frequency and amplitude output.

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“…Sensor systems used for motion monitoring include wireless motion sensors (accelerometers and gyrometers) [15,16], camera systems (thermal, depth and color cameras) [9,17], ultrasound systems [18], and satellite positioning systems [12][13][14]. Specific methods are used for respiratory data processing [19] as well.…”

Section: Introductionmentioning

confidence: 99%

“…There are very wide applications of motion monitoring systems, including gait analysis [21][22][23][24][25][26], motion evaluation [27][28][29][30][31][32], stroke patients monitoring [18,33], recognition of physical activities [34][35][36][37][38], breathing [39], and detection of motion disorders during sleep [40]. The combination of accelerometer sensors at different body locations in possible combination with GPS and geographical information systems is useful in improving movement monitoring of humans [41], assessing road surface roughness [10], and activity recognition [30] as well.…”

Section: Introductionmentioning

confidence: 99%

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Charvátová

Procházka

Vyšata

2020

Sensors

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Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands ⟨ 3 , 8 ⟩ and ⟨ 8 , 15 ⟩ Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

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A Multi-Channel Ultrasound System for Non-Contact Heart Rate Monitoring

Cited by 27 publications

References 57 publications

Using smart speakers to contactlessly monitor heart rhythms

Using smart speakers to contactlessly monitor heart rhythms

Design and Performance Analysis of an Ultrasonic System for Health Monitoring of Concrete Structure

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

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