Unobtrusive monitoring of sedentary behaviors with fusion of bluetooth and ballistocardiogram signals

Wu, Chuanmin; Wang, Binghe; Shen, Gang

doi:10.1016/j.ymeth.2021.06.001

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…BCG can be a non-electrode contact, non-binding, non-invasive monitoring technology, and has been widely used in biomedical engineering [17][18][19]. It is the description of the small displacements of the human body caused by heart activities [16]. The rhythm of BCG is consistent with ECG, and the measured heart rate extracted from BCG signal agrees well with the commercial physiologic device [20].…”

Section: Descriptions Charactersmentioning

confidence: 57%

“…Recently, ballistocardiogram (BCG) signals have drawn extensive interests from the investigators in the field of health monitoring [16]. BCG can be a non-electrode contact, non-binding, non-invasive monitoring technology, and has been widely used in biomedical engineering [17][18][19].…”

Section: Descriptions Charactersmentioning

confidence: 99%

“…In recent years, sensors have been widely used to detect BCG signals including two kinds of popular sensors, piezoelectric film sensors and optical fiber sensors. Both of them can perform interference-free measurement and obtain human heart rate information without burden [16,20]. ECG signal has been widely used for brain fatigue detection [23][24][25][26], but lacks the application of a BCG signal in brain fatigue evaluation.…”

Section: Descriptions Charactersmentioning

confidence: 99%

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A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Yang

et al. 2021

Healthcare

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Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

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confidence: 57%

Section: Descriptions Charactersmentioning

confidence: 99%

Section: Descriptions Charactersmentioning

confidence: 99%

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A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Yang

et al. 2021

Healthcare

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“…Reference [7] used clustering algorithm to achieve adaptive matching of BCG waveform and calculated heart rate by Hilbert transform; Reference [8] and Reference [9] used discrete wavelet transformation and continuous wavelet transformation methods to extract heart rate information from BCG signals, respectively; Reference [10] combined the empirical mode decomposition (EMD) and independent component analysis (ICA) to achieve noise reduction of BCG signal; Reference [11] proposes a BCG denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with permutation entropy (PE). Wu et al fused Bluetooth signals and BCG signals to monitor sedentary behavior by extracting local spectral features and signal differences [12]; Ibrahim Sadek et al investigated the effectiveness of three heart rate detection algorithms, namely maximal overlap discrete wavelet transform (MODWT-MRA), continuous wavelet transform (CWT) and template matching (TM), on four independent BCG datasets [13]. Chun-Liang Lin et al designed a BCG acquisition system to estimate heart rate and respiratory rate by noise reduction and regression analysis [14].…”

Section: Introductionmentioning

confidence: 99%

Heart rate detection of ballistocardiogram based on improved DAE and template matching method

Mou,

Han,

Chen

2023

Eng. Res. Express

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Ballistocardiogram (BCG) is a non-invasive physiological signal detection method that can be used for non-contact detection of resting heart rate (RHR) and has been widely used in human health monitoring. However, the BCG signal is vulnerable to noise, making it challenging to accurately measure heart rate. In this paper, we propose a noise reduction model for the BCG signal based on Gramian Angular Field (GAF) and an improved Denoising Autoencoder (DAE), referred to as the GDAE model, to accurately detect heart rate in noise-contaminated signals. First, the Gramian Angular Field transform is used to convert the one-dimensional BCG signal into two-dimensional image information, highlighting the difference between the signal centroid information and noise information; after that, the transformed image is denoised by the Denoising Autoencoder to obtain a denoised BCG signal. After noise reduction, the heart rate of the subject is calculated using the adaptive template matching method. The test proves that under the strong noise interference, the proposed method improves the recall by 6.87% and the accuracy by 6.02% compared with the traditional method, indicating a better detection effect. Furthermore, the comparison test shows that the GDAE model has a significant noise reduction effect on the BCG signal, which improves the practicality of the BCG method for heart rate detection.

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Improving ballistocardiogram-based continuous heart rate variability monitoring: A self-supervised learning approach

Wu,

Qiu,

Shen

2024

Biomedical Signal Processing and Control

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Unobtrusive monitoring of sedentary behaviors with fusion of bluetooth and ballistocardiogram signals

Cited by 3 publications

References 32 publications

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Heart rate detection of ballistocardiogram based on improved DAE and template matching method

Improving ballistocardiogram-based continuous heart rate variability monitoring: A self-supervised learning approach

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