To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to (1) capture PPG signals and (2) wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices.

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The principle of automatic welding machine (AWM) for air conditioning flange is introduced. The architecture of the control system of AWM realizes the multi spindle linkage control. The communication between PLC and servo driver is built based on CANopen protocol, and the communication between PLC and touch screen is set up based on Modbus RTU protocol. To meet the welding machine spindle coordination control requirements, the PLC program is designed by Delta ISPsoft programming software, and simplified by the application of sequential function chart (SFC) and function block. The human machine interaction (HMI) is developed by Xinjie Touchwin. Linear interpolation algorithm (LIA) is used to prevent the welding defects on the air conditioning flange corner thus it improves the welding quality and realizes the straight path of welding torch from the original to starting welding point and from the end welding point to the original simultaneously. The AWM for air conditioning flange can replace the manual welding and has a broad application prospect in many areas.