A waveguide Bragg grating (WBG) provides a flexible way for measurement, and it could even be used to measure body temperature like e-skin. We designed and compared three structures of WBG with the grating period, etching depth, and duty cycle. The two-sided WBG was fabricated. An experimental platform based on photonic integrated interrogator was set up and the experiment on the two-sided WBG was performed. Results show that the two-sided WBG can be used to measure temperature changes over the range of 35–42 °C, with a temperature measurement error of 0.1 °C. This approach has the potential to facilitate application of such a silicon-on-insulator (SOI) WBG photonic sensor to wearable technology and realize the measurement of human temperature.

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Wearable technology constitutes a pioneering and leading innovation and a market development platform worldwide for technologies worn close to the body. Wearable optical fiber sensors have the most value for advanced multiparameter sensing in digital health monitoring systems. We demonstrated the first example of a fully integrated optical interrogator. By integrating all the optical components on a silicon photonic chip, we realized a stable, miniaturized and low-cost optical interrogator for the continuous, dynamic, and long-term acquisition of human physiological signals. The interrogator was integrated in a wristband, enabling the detection of body temperature and heart sounds. Our study paves the way for the development of watch-sized integrated wearable optical interrogators with potential applications in health monitoring and can be directly exploited for the customized design of ultraminiaturized optical interrogator systems.

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Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.