This paper presents a novel respiration sensor based on multiple in‐fiber microholes drilled along a plastic optical fiber (POF) that is stitched to a waist‐band wrapped around the patient's abdomen. The bending loss of a POF with a single microhole is analyzed in order to enhance the sensor sensitivity. Several sensors were fabricated using a 1.5 mm‐diameter POF with different numbers and spacing of 0.7 mm‐diameter microholes. The sensitivity of the respiration sensor increased when increasing the number and spacing of the microholes, where the POF respiration sensor with seven 0.7 mm‐diameter in‐fiber microholes spaced at 15 mm showed the highest sensitivity and sufficient strength for respiratory monitoring. Thus, the experimental results confirmed that a POF sensor based on in‐fiber microholes can be effectively used to monitor the respiration of patients.

Objective: This study sought to explore the novel use of artificial neural networks (ANNs) to develop a contrast-enhanced computed tomography (CT) angiographic (CECTA) protocol based on ideal bolus geometry. Materials and Methods:An aortic phantom connected to a closed-circuit pulsatile flow system was developed to simulate the bolus geometry of the human abdominal aorta. A total of 135 CECTA datasets were obtained using a 16-row multidetector CT scanner, and timeenhancement curves (TECs) were generated using varying input conditions including heart rate (HR), iodine delivery rate (IDR) and concentration (IC), and tube potential (kVP). Time points and density values including peak enhancement (PE) and time-to-peak (TTP) were assessed as a function of injection and scan protocols. Statistical analysis was performed using correlation and linear regression analyses. By using data from phantom experiments, machine learning produced networks between four input (HR, IC, IDR, and kilovoltage) and five output [TTP-time-to-foot (TTF), PE, (PE)/(TTP−TTF), maximal-upslope-gradient (MUG), and peakplateau-length (PPL)] conditions. The bolus geometry index was defined as (TTP−TTF)/∑(PE, (PE)/(TTP−TTF), MUG, PPL). The lowest bolus geometry index value was considered ideal in ANN testing. Results:The geometric changes on TECs were observed based on changes in HR, IDR, IC, and kilovoltage value. PE was closely related to IDR (B=17.471) and kVp (B=-0.208) (corrected R 2 =0.919; all p<0.001). TTF, TTP, and PPL were related to HR and IDR, respectively. HR and IDR remained contributing factors after multiple linear regression analysis (corrected R 2 =0.901, 0.815, and 0.363; all p<0.001). Among 39690 total datasets produced following ANN training, the combination of IC, HR, tube potential, and IDR in the 38010th dataset resulted in the lowest bolus geometry index. Tables of input variables are presented after modification to clinically acceptable ranges.Conclusion: ANN of phantom experiments showed the potential to determine optimal CECTA parameters for ideal bolus geometry individualized for each subject.

Snoring lowers the quality of sleep, causing many secondary diseases. In response, various types of preventive devices were manufactured, but most of them were far from actual snoring prevention by only sensing snoring and giving feedback. In this study, we proposed a new method to prevent snoring by adjusting the posture during sleep by widening the oropharynx space. An increase in the oropharynx area was confirmed through the expansion of the cervical spine, and a dedicated pillow that can extend through an angle of up to 20° was manufactured. Through this developed method, it was possible to easily extend the cervical spine angle in a supine position to the user, and the frequency of snoring was then tested. As a result, it was confirmed that by using the pillow with an expansion angle of 20° or more, snoring did not occur. Furthermore, looking at the evaluation results of the subjective levels of satisfaction, sleep-related items received an average of 5.9 or higher, and function-related items received high scores with an average of 5.7. We can confirm that the reliability of performance evaluation will be dramatically improved if the scope of the subject group is expanded to include various body types, ages, and genders and conduct performance evaluations for each group.

No abstract

No abstract