Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method.

Drunk driving accidents have been rapidly increasing in recent times. Although the statistics show a decreasing trend in recent years, reports of drunk driving accidents are often seen in the news. To assess vehicle operators for drunk driving, the police still use breath-alcohol testers as the primary method. However, a certified instrument to measure alcohol consumption is expensive, and the mouthpiece used in the instrument is a consumable. Moreover, the breath detection method used involves contact measurement, which may cause hygiene concerns. To achieve more convenient and accurate detection, many researchers have proposed methods to replace the traditional breath-type measurement instruments. The present study proposes a two-stage neural network for recognition of drunk driving: the first stage uses the simplified VGG network to determine the age range of the subject, and the second stage uses the simplified Dense-Net to identify the facial features of drunk driving. The age discrimination stage obtained an accuracy of 86.36%. In addition, in drunk driving recognition tests among various age groups (18–30, 31–50, and ≥51 years), accuracies of 94%, 83%, and 81% were obtained, respectively. The overall system also showed a high accuracy of 89.62% and 87.44%, which proves the robustness of the system while supporting its practical application.

This paper presents an ac–dc rectifier for piezoelectric vibration energy harvesting application. The proposed rectifier is designed and implemented with TSMC 0.25 μm 1P3M 5 V CMOS process. Active control is proposed to eliminate the voltage drop from the traditional diode forward voltage and the reverse current from the output capacitor to input source. The simultaneously turn on and oscillation on power MOS MN1–MN2 gate signal VGN1–VGN2 has been overcome by a 40ns delay time which generated by a non-overlapping control. The experimental results indicated maximum power efficiency 95% and voltage conversion ratio 99% are achieved at 20kΩ and 200kΩ loading resistor.

Background: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)–based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. Methods: The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. Results: Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. Conclusions: We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making.