Traditional cuff-based blood pressure measurement methods suffer from issues such as intermittency and applicability, while cuff-less continuous blood pressure estimation techniques are increasingly gaining attention due to their non-invasive and continuous monitoring advantages. In this paper, aiming at the challenges faced by existing cuff-less continuous blood pressure estimation models in terms of accuracy, data requirements, and generalization ability, a series of innovative approaches are proposed. Deep learning techniques are introduced to design an end-to-end blood pressure estimation model with high accuracy, ease of training, and strong generalization ability. To address the insufficient accuracy of traditional neural networks in cuff-less continuous blood pressure estimation, we propose an end-to-end, beat-to-beat blood pressure estimation model that combines the temporal convolutional network (TCN) and convolutional block attention module (CBAM). By enhancing the model’s ability to process time series data and focus on key features of photoplethysmography (PPG), the blood pressure estimation accuracy during the resting state is significantly improved. The absolute mean error and standard deviation of systolic blood pressure (SBP) estimation using the algorithm in this chapter on the University of California, Irvine (UCI) physiological signal dataset are 5.3482 mmHg and 8.3410 mmHg, respectively, which are superior to other deep learning models based on convolutional neural network and recurrent neural network architectures.

Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.