No abstract

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO2 in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

Dynamic loading of software components (e.g., libraries or modules) is a widely used mechanism for improved system modularity and flexibility. Correct component resolution is critical for reliable and secure software execution, however, programming mistakes may lead to unintended or even malicious components to be resolved and loaded. In particular, dynamic loading can be hijacked by placing an arbitrary file with the specified name in a directory searched before resolving the target component. Although this issue has been known for quite some time, it was not considered serious because exploiting it requires access to the local file system on the vulnerable host. Recently such vulnerabilities started to receive considerable attention as their remote exploitation became realistic; it is now important to detect and fix these vulnerabilities.In this paper, we present the first automated technique to detect vulnerable and unsafe dynamic component loadings. Our analysis has two phases: 1) apply dynamic binary instrumentation to collect runtime information on component loading (online phase); and 2) analyze the collected information to detect vulnerable component loadings (offline phase). For evaluation, we implemented our technique to detect vulnerable and unsafe DLL loadings in popular Microsoft Windows software. Our results show that unsafe DLL loading is prevalent and can lead to serious security threats. Our tool detected more than 1,700 unsafe DLL loadings in 28 widely used software and discovered serious attack vectors for remote code execution. Microsoft has opened a Microsoft Security Response Center (MSRC) case on our reported issues and is working with us and other affected software vendors to develop necessary patches.

Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter (PO) is one of the most common clinical devices for critical medical care. In this paper, we explain how we developed a wearable PO. We propose a new electronic circuit based on an analog filter that can separate red and green photoplethysmography (PPG) signals, acquire clean PPG signals, and estimate the pulse rate (PR) and peripheral capillary oxygen saturation (SpO2). We propose a PR and SpO2 measurement algorithm with and without the motion artifact. We consider three types of motion artifacts with our acquired clean PPG signal from our proposed electronic circuit. To evaluate our proposed algorithm, we measured the accuracy of our estimated SpO2 and PR. To evaluate the quality of our estimated PR (bpm) and SpO2 (%) with and without the finger motion artifact, we used the quality evaluation metrics: mean absolute percentage error (MAPE), mean absolute error (MAE), and reference closeness factor (RCF). Without the finger motion condition, we found that our proposed wearable PO device achieved an average 2.81% MAPE, 2.08 bpm MAE, 0.97 RCF, and 98.96% SpO2 accuracy. With a finger motion, the proposed wearable PO device achieved an average 4.5% MAPE, 3.66 bpm MAE, 0.96 RCF, and 96.88% SpO2 accuracy. We also show a comparison of our proposed PO device with a commercial Fingertip PO (FPO) device. We have found that our proposed PO device performs better than the commercial FPO device under finger motion conditions. To demonstrate the implementation of our wearable PO, we developed a smartphone app to allow the PO device to share PPG signals, PR, and SpO2 through Bluetooth communication. We also show the possible applications of our proposed PO as a wearable, hand-held PO device, and a PPG signal acquisition system.

Glycated hemoglobin (HbA1c) is an important factor in monitoring diabetes. Since the glycated hemoglobin value reflects the average blood glucose level over 3 months, it is not affected by exercise or food intake immediately prior to measurement. Thus, it is used as the most basic measure of evaluating blood-glucose control over a certain period and predicting the occurrence of long-term complications due to diabetes. However, as the existing measurement methods are invasive, there is a burden on the measurement subject who has to endure increased blood gathering and exposure to the risk of secondary infections. To overcome this problem, we propose a machine-learning-based noninvasive estimation method in this study using photoplethysmography (PPG) signals. First, the development of the device used to acquire the PPG signals is described in detail. Thereafter, discriminative and effective features are extracted from the acquired PPG signals using the device, and a machine-learning algorithm is used to estimate the glycated hemoglobin value from the extracted features. Finally, the performance of the proposed method is evaluated by comparison with existing model-based methods.