CardioGuard: A Brassiere-based Reliable ECG Monitoring Sensor System for Supporting Daily Smartphone Healthcare Applications

We propose CardioGuard, a brassiere-based reliable electrocardiogram (ECG) monitoring sensor system, for supporting daily smartphone healthcare applications. It is designed to satisfy two key requirements for user-unobtrusive daily ECG monitoring: reliability of ECG sensing and usability of the sensor. The system is validated through extensive evaluations. The evaluation results showed that the CardioGuard sensor reliably measure the ECG during 12 representative daily activities including diverse movement levels; 89.53% of QRS peaks were detected on average. The questionnaire-based user study with 15 participants showed that the CardioGuard sensor was comfortable and unobtrusive. Additionally, the signal-to-noise ratio test and the washing durability test were conducted to show the high-quality sensing of the proposed sensor and its physical durability in practical use, respectively

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use

Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators' dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed

Replenishment of microRNA-188-5p restores the synaptic and cognitive deficits in 5XFAD Mouse Model of Alzheimer’s Disease

MicroRNAs have emerged as key factors in development, neurogenesis and synaptic functions in the central nervous system. In the present study, we investigated a pathophysiological significance of microRNA-188-5p (miR-188-5p) in Alzheimer’s disease (AD). We found that oligomeric Aβ(1-42) treatment diminished miR-188-5p expression in primary hippocampal neuron cultures and that miR-188-5p rescued the Aβ(1-42)-mediated synapse elimination and synaptic dysfunctions. Moreover, the impairments in cognitive function and synaptic transmission observed in 7-month-old five familial AD (5XFAD) transgenic mice, were ameliorated via viral-mediated expression of miR-188-5p. miR-188-5p expression was down-regulated in the brain tissues from AD patients and 5XFAD mice. The addition of miR-188-5p rescued the reduction in dendritic spine density in the primary hippocampal neurons treated with oligomeric Aβ(1-42) and cultured from 5XFAD mice. The reduction in the frequency of mEPSCs was also restored by addition of miR-188-5p. The impairments in basal fEPSPs and cognition observed in 7-month-old 5XFAD mice were ameliorated via the viral-mediated expression of miR-188-5p in the hippocampus. Furthermore, we found that miR-188 expression is CREB-dependent. Taken together, our results suggest that dysregulation of miR-188-5p expression contributes to the pathogenesis of AD by inducing synaptic dysfunction and cognitive deficits associated with Aβ-mediated pathophysiology in the disease

Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed

Effects of Oxygen Flow Rate on Metal-to-Insulator Transition Characteristics in NbO_x-Based Selectors

In this work, NbOx-based selector devices were fabricated by sputtering deposition systems. Metal-to-insulator transition characteristics of the device samples were investigated depending on the oxygen flow rate (3.5, 4.5, and 5.5 sccm) and the deposition time. The device stack was scanned by transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). The yields, including MIT, nonlinear, and Ohmic, in working devices with different deposition conditions were also evaluated. Moreover, we observed the trend in yield values as a function of selectivity. In addition, the current–voltage (I–V) curves were characterized in terms of DC and pulse endurance. Finally, the switching speed and operating energies were obtained by applying a triangular pulse on the devices, and the recovery time and drift-free characteristics were obtained by the paired pulses

VitaMon: Measuring heart rate variability using smartphone front camera

© 2019 ACM.We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network models to extract both spatial and temporal information of the video to reconstruct a pulse waveform signal that is optimized for estimating IBI. We evaluated VitaMon with a dataset collected from 30 participants under various conditions involving different light intensity levels and motion artifacts. With the 15 fps video input (66.67 ms time resolution), VitaMon can measure IBI with an average error of 14.26 ms and 21.65 ms using personal and general model respectively. HRV features including geometry Poincare plot, time- and frequency-domain features extracted from the IBI measurement all have high correlation with the reference signal.Y

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society