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Kuang RF, Wang Z, Ma L et al. Smart photonic wristband for pulse wave monitoring. Opto-Electron Sci 3, 240009 (2024). doi: 10.29026/oes.2024.240009
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Abstract
Real-time acquisition of human pulse signals in daily life is clinically important for cardiovascular disease monitoring and diagnosis. Here, we propose a smart photonic wristband for pulse signal monitoring based on speckle pattern analysis with a polymer optical fiber (POF) integrated into a sports wristband. Several different speckle pattern processing algorithms and POFs with different core diameters were evaluated. The results indicated that the smart photonic wristband had a high signal-to-noise ratio and low latency, with the measurement error controlled at approximately 3.7%. This optimized pulse signal could be used for further medical diagnosis and was capable of objectively monitoring subtle pulse signal changes, such as the pulse waveform at different positions of Cunkou and pulse waveforms before and after exercise. With the assistance of artificial intelligence (AI), functions such as gesture recognition have been realized through the established prediction model by processing pulse signals, in which the recognition accuracy reaches 95%. Our AI-assisted smart photonic wristband has potential applications for clinical treatment of cardiovascular diseases and home monitoring, paving the way for medical Internet of Things-enabled smart systems.-
Keywords:
- smart healthcare /
- specklegram /
- pulse monitoring /
- gesture recognition /
- artificial intelligence /
- wearable sensor
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References
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Supplementary information for Smart photonic wristband for pulse wave monitoring 
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Kuang RF, Wang Z, Ma L et al. Smart photonic wristband for pulse wave monitoring. Opto-Electron Sci 3, 240009 (2024). doi: 10.29026/oes.2024.240009
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Figure 1.
Schematic diagram of the overall design. The diagram illustrates (a) the sensing principle of the smart photonic wristband. (b) The physical view of the wristband POF-based sensor. (c) The internal structure of the sensor. (d) The monitoring system, which includes a laser (light source), POF wristband sensor, signal acquisition, data processing, pulse rate computation, cloud terminal, and an artificial intelligence processor.
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Figure 2.
Scheme of the specklegram image processing. The middle block diagram shows an example of the frame-to-frame difference under external continuous disturbances.
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Figure 3.
Performance testing and optimization of the smart photonic wristband. (a) Results of a preliminary experiment: (I) Time-domain response of human pulse signal; (II) Power spectral density corresponding to the pulse signal. (b) Pulse waveforms from different processing methods. The method names corresponding to subscripts (I) to (VI) are normalized inner-product coefficient, zero-mean normalized cross-correlation, first-order moment, gray-level co-occurrence matrix, mutual information, and the sum of squared differences, respectively. (c) ECG reference signal acquisition device. (I) Schematic diagram; (II) Actual photograph. (d) Signal response in the frequency spectrum of different processing methods. The method names corresponding to subscripts (I) to (VI) are normalized inner-product coefficient, zero-mean normalized cross-correlation, first-order moment, gray-level co-occurrence matrix, mutual information, and the sum of squared differences, respectively.
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Figure 4.
Processing algorithms for speckle patterns using various methods.
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Figure 5.
(a) Speckle images formed by the POFs with different core diameters: (I) 250 µm; (II) 500 µm; (III) 750 µm; (VI)
1000 µm. (b) Example of a 10-s long comparison between a processed signal and a reference signal, where A is the T-wave peak in the ECG signal and B is the percussion-wave peak in the pulse signal. (c) Some results of the tested different core diameters, where the left ordinate is the time delay and the right ordinate is the SNR. (d) Signal response in the frequency domain with the different core diameters. -
Figure 6.
(a) (I) Human pulse signal diagram acquired under the optimal method; (II) Detail within 10 s; A is the percussion wave (PW), B is the dicrotic notch, and C is the dicrotic wave. (b) Optical power-pressure curves of POF sensors in indentation tests. (c) Relationship between optical power and bending radius of POF sensor in bending tests.
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Figure 7.
(a) Diagnostic method of the Cunkou illustration. (I) Schematic illustrating the position of Cunkou. (II) Pulse waveforms at different positions of Cun, Guan, and Chi. (b) Pulse monitoring pre- and post-exercise. (I) Exercise example schematic. (II) Pulse waveforms at different exercise durations. (III) Waveform details in different states.
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Figure 8.
Flowchart of the neural network model and classification results. (a) Neural network processing flow of the pulse wave signal. (b) Confusion matrix resulting from data processing.
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Figure 9.
A snapshot of the smartphone application for monitored data visualization.
