Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 104 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 104 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.
Endometrial cancer (EC) is one of the most common gynecological malignancies, with an increasing incidence rate worldwide. Accurate segmentation of lesion areas in computed tomography (CT) images is a critical step in assisting clinical diagnosis. In this study, we propose a novel deep learning-based segmentation model, termed spatial choice and weight union network (SCWU-Net), which incorporates two newly designed modules: the spatial selection module (SSM) and the combination weight module (CWM). The SSM enhances the model’s ability to capture contextual information through deep convolutional blocks, while the CWM, based on joint attention mechanisms, is employed within the skip connections to further boost segmentation performance. By integrating the strengths of both modules into a U-shaped multi-scale architecture, the model achieves precise segmentation of EC lesion regions. Experimental results on a public dataset demonstrate that SCWU-Net achieves a Dice similarity coefficient (DSC) of 82.98%, an intersection over union (IoU) of 78.63%, a precision of 92.36%, and a recall of 84.10%, its overall performance is significantly outperforming other state-of-the-art models. This study enhances the accuracy of lesion segmentation in EC CT images and holds potential clinical value for the auxiliary diagnosis of endometrial cancer.