Emotion recognition is vital for mental and physical health, but current methods often rely on active user participation (e.g., questionnaires) or wearable sensors, limiting their real-world adoption. This paper proposes a non-contact ballistocardiogram (BCG) based cross-subject emotion recognition framework. Subject-specific data distribution variations challenge model generalizability across domains. For this reason, we propose an LSCS-BDA method, extending Balanced Distribution Adaptation (BDA) with a Label Similarity Clustering for Selection (LSCS) module to optimize source domain selection. After feature extraction and baseline normalization, only the source domain individuals that are more similar to the target are included in the final source domain set through our method’s LSCS module (reducing training data to 9.78%), and finally the transfer is completed by the BDA algorithm. The experimental results show that the classification accuracy of our proposed method for three emotions of positive, negative, and neutral is 70.38%, which achieves higher accuracy and faster model training than classical transfer algorithms. The LSCS module in our method can be extended to other transfer learning methods. Our approach offers a new idea for contactless emotion recognition based on BCG and contributes to advancing its applications.

Accurate and portable Blood Pressure (BP) monitoring is vital for managing cardiovascular diseases. However, existing wearable continuous BP monitoring technologies are often inaccurate and rely on external calibration, limiting their practical application in continuous BP monitoring. To address this challenge, we have developed a Wearable continuous non-invasive BP Monitor (WeBPM) equipped with a finger cuff sensor, capable of monitoring BP continuously and accurately within medical-grade precision. WeBPM integrates advanced finger oscillographic BP measurement technology to provide reliable self-calibration functionality. Moreover, Pulsatile Cycle Volume Adjustment Method (PCVAM) we proposed for the closed-loop phase can continuously track changes in vasomotor tone under a controlled frequency based on pulsatile cycles, thereby enabling continuous BP measurement. In comparative experiments with the Nexfin monitor, WeBPM demonstrates excellent performance in induced dynamic BP experiments, with measurement errors of (–1.4 $\pm$ 6.24) mmHg for Systolic BP (SBP) and (–0.82 $\pm$ 4.83) mmHg for Diastolic BP (DBP). Additionally, compared to clinical invasive reference measurements, WeBPM’s SBP and DBP measurement errors are (–1.74 $\pm$ 4.9) mmHg and (0.37 $\pm$ 3.28) mmHg, respectively, further proving its outstanding performance. These results highlight WeBPM’s potential in personalized health management and remote monitoring, offering a new solution for continuous non-invasive BP monitoring.