BCG algorithm for unobtrusive heart rate monitoring

Pino, Esteban J.; Chávez, Javier A. P.; Aqueveque, Pablo

doi:10.1109/hic.2017.8227614

Cited by 21 publications

(17 citation statements)

References 13 publications

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“…Given one frame, we calculate mean R-R time interval T and then HR is computed as 60/T . Mean absolute error (MAE) and standard deviation of absolute error (STD) are used to assess the performance of the proposed method, which are given by (9) and (10).…”

Section: B Performance Metricsmentioning

confidence: 99%

“…Table I gives MAE and STD for every subject using the proposed method. The methods from [5], [8], [9] are implemented as well. MAE and STD using these methods for HR estimation are also listed in Table I.…”

Section: B Performance Metricsmentioning

confidence: 99%

“…The envelope usally appears irregularly when BCG signal dose not have consistent pattern. Pino et al [9] put forward multiple smoothed length transform (SLT) method for heart rate estimation from BCG. It selects four fixed windows for SLT computation.…”

Section: Introductionmentioning

confidence: 99%

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Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder

Xie

Wang

Lian

2018

2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

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This paper presents a robust method to monitor heart rate (HR) from BCG (Ballistocardiography) signal, which is acquired from the sensor embedded in a chair or a mattress. The proposed algorithm addresses the shortfalls in traditional Fast Fourier Transform (FFT) based approaches by introducing Hilbert Transform to extract the pulse envelope that models the repetition of J-peaks in BCG signal. The frequency resolution is further enhanced by applying FFT and phase vocoder to the pulse envelope. The performance of the proposed algorithm is verified by experiment from 7 subjects. For HR estimation, mean absolute error (MAE) of 0.90 beats per minute (BPM) and standard deviation of absolute error (STD) of 1.14 BPM are obtained. Pearson correlation coefficient between estimated HR and ground truth HR of 0.98 is also achieved.

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Section: B Performance Metricsmentioning

confidence: 99%

Section: B Performance Metricsmentioning

confidence: 99%

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Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder

Xie

Wang

Lian

2018

2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

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“…It is one of the oldest non-invasive methods for cardiac-respiratory monitoring and can be used to get information about the activity of the heart, its condition and breathing patterns. Its graphical representation consists of the action-reaction force caused by the heartbeat and the pump of blood through the aorta [31]. The IJK wave complex from the BCG represents the ejection phase of the cardiac cycle.…”

Section: Cardiovascular Monitoringmentioning

confidence: 99%

“…The main devices requiring mechanical contact are piezoelectric sensors, load cells, sternal accelerometers and electromechanical film sensors (EMFi). The robustness of BCG monitoring systems based on EMFi sensors has been evidenced in its large number of implementations [31,[34][35][36]. However, the adoption of this solution in medical facilities is still limited in present days.…”

Section: Cardiovascular Monitoringmentioning

confidence: 99%

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Rodrigues

Postolache

Cercas

2020

Sensors

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Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

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