Browsing by Author "Chong, Jo Woon (TTU)"
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Item A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts(2019) Tabei, Fatemehsadat (TTU); Zaman, Rifat (TTU); Foysal, Kamrul H. (TTU); Kumar, Rajnish (TTU); Kim, Yeesock; Chong, Jo Woon (TTU)The advent of smartphones has advanced the use of embedded sensors to acquire various physiological information. For example, smartphone camera sensors and accelerometers can provide heart rhythm signals to the subjects, while microphones can give respiratory signals. However, the acquired smartphone-based physiological signals are more vulnerable to motion and noise artifacts (MNAs) compared to using medical devices, since subjects need to hold the smartphone with proper contact to the smartphone camera and lens stably and tightly for a duration of time without any movement in the hand or finger. This results in more MNA than traditional methods, such as placing a finger inside a tightly enclosed pulse oximeter to get PPG signals, which provides stable contact between the sensor and the subject’s finger. Moreover, a smartphone lens does not block ambient light in an effective way, while pulse oximeters are designed to block the ambient light effectively. In this paper, we propose a novel diversity method for smartphone signals that reduces the effect of MNAs during heart rhythm signal detection by 1) acquiring two heterogeneous signals from a color intensity signal and a fingertip movement signal, and 2) selecting the less MNA-corrupted signal of the two signals. The proposed method has advantages in that 1) diversity gain can be obtained from the two heterogeneous signals when one signal is clean while the other signal is corrupted, and 2) acquisition of the two heterogeneous signals does not double the acquisition procedure but maintains a single acquisition procedure, since two heterogeneous signals can be obtained from a single smartphone camera recording. In our diversity method, we propose to choose the better signal based on the signal quality indices (SQIs), i.e., standard deviation of instantaneous heart rate (STD–HR), root mean square of the successive differences of peak-to-peak time intervals (RMSSD–T), and standard deviation of peak values (STD–PV). As a performance metric evaluating the proposed diversity method, the ratio of usable period is considered. Experimental results show that our diversity method increases the usable period 19.53% and 6.25% compared to the color intensity or the fingertip movement signals only, respectively.Item A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals(2018) Tabei, Fatemehsadat (TTU); Kumar, Rajnish (TTU); Phan, Tra Nguyen (TTU); McManus, David D.; Chong, Jo Woon (TTU)Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, the PPG signals are sensitive to motion and noise artifacts (MNAs), especially when they are obtained from smartphone cameras. Moreover, the PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. In this paper, a concept of the probabilistic neural network is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As for the performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity, and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.Item Analyte quantity detection from lateral flow assay using a smartphone(2019) Foysal, Kamrul H. (TTU); Seo, Sung Eun; Kim, Min Ju; Kwon, Oh Seok; Chong, Jo Woon (TTU)Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.Item Body size measurement using a smartphone(2021) Foysal, Kamrul Hasan (TTU); Chang, Hyo Jung (TTU); Bruess, Francine (TTU); Chong, Jo Woon (TTU)Measuring body sizes accurately and rapidly for optimal garment fit detection has been a challenge for fashion retailers. Especially for apparel e-commerce, there is an increasing need for digital and convenient ways to obtain body measurements to provide their customers with correct-fitting products. However, the currently available methods depend on cumbersome and complex 3D reconstruction-based approaches. In this paper, we propose a novel smartphone-based body size measurement method that does not require any additional objects of a known size as a reference when acquiring a subject’s body image using a smartphone. The novelty of our proposed method is that it acquires measurement positions using body proportions and machine learning techniques, and it performs 3D reconstruction of the body using measurements obtained from two silhouette images. We applied our proposed method to measure body sizes (i.e., waist, lower hip, and thigh circumferences) of males and females for selecting well-fitted pants. The experimental results show that our proposed method gives an accuracy of 95.59% on average when estimating the size of the waist, lower hip, and thigh circumferences. Our proposed method is expected to solve issues with digital body measurements and provide a convenient garment fit detection solution for online shopping.Item Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period(2022) Li, Yifan (TTU); Pang, Alan W (TTUHSC); Zeitouni, Ferris (TTUHSC); Mateja, Kirby (TTUHSC); Griswold, John A (TTUHSC); Chong, Jo Woon (TTU)The abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians’ experience and expertise. Additionally, no correlation has been shown between these patients’ inhalation injury grades and outcomes. In this paper, we propose a novel inhalation injury grading method which uses deep learning algorithms in bronchoscopy images to determine the injury grade from the carbonaceous deposits, blistering, and fibrin casts in the bronchoscopy images. The proposed method adopts transfer learning and data augmentation concepts to enhance the accuracy performance to avoid overfitting. We tested our proposed model on the bronchoscopy images acquired from eighteen patients who had suffered inhalation injuries, with the degree of severity 1, 2, 3, 4, 5, or 6. As performance metrics, we consider accuracy, sensitivity, specificity, F-1 score, and precision. Experimental results show that our proposed method, with both transfer learning and data augmentation components, provides an overall 86.11% accuracy. Moreover, the experimental results also show that the performance of the proposed method outperforms the method without transfer learning or data augmentation.Item Novel Fingertip Image-Based Heart Rate Detection Methods for a Smartphone(2017) Zaman, Rifat (TTU); Cho, Chae Ho (TTU); Hartmann-Vaccarezza, Konrad; Phan, Tra Nguyen (TTU); Yoon, Gwonchan (TTU); Chong, Jo Woon (TTU)We hypothesize that our fingertip image-based heart rate detection methods using smartphone reliably detect the heart rhythm and rate of subjects. We propose fingertip curve line movement-based and fingertip image intensity-based detection methods, which both use the movement of successive fingertip images obtained from smartphone cameras. To investigate the performance of the proposed methods, heart rhythm and rate of the proposed methods are compared to those of the conventional method, which is based on average image pixel intensity. Using a smartphone, we collected 120 s pulsatile time series data from each recruited subject. The results show that the proposed fingertip curve line movement-based method detects heart rate with a maximum deviation of 0.0832 Hz and 0.124 Hz using time- and frequency-domain based estimation, respectively, compared to the conventional method. Moreover, another proposed fingertip image intensity-based method detects heart rate with a maximum deviation of 0.125 Hz and 0.03 Hz using time- and frequency-based estimation, respectively.Item Novel image processing method for detecting strep throat (Streptococcal pharyngitis) using smartphone(2019) Askarian, Behnam (TTU); Yoo, Seung Chul; Chong, Jo Woon (TTU)In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k-fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average.Item Particle Swarm Optimization for Active Structural Control of Highway Bridges Subjected to Impact Loading(2018) Hughes, Jake Edmond; Kim, Yeesock; Chong, Jo Woon (TTU); Kim, ChangwonThe application of active structural control technology to highway bridge structures subjected to high-impact loadings is investigated. The effects of high-impact loads on infrastructure, like heavy vehicle collisions with bridge piers, have not been studied as much as seismic load effects on structures. Due to this lack of research regarding impact loads and structural control, a focused study on the application of active control devices to infrastructure after impact events can provide valuable results and conclusions. This research applies active structural control to an idealized two-span, continuous girder, concrete highway bridge structure. The idealization of a highway bridge structure as a two degree-of-freedom structural system is used to investigate the effectiveness of control devices installed between the bridge pier and deck, the two degrees of freedom. The control devices are fixed to bracing between the bridge pier and girders and controlled by the proportional-integral-derivative (PID) control. The PID control gains are optimized by both the Ziegler-Nichols ultimate sensitivity method (USM) and a new method for this impact load application called particle swarm optimization (PSO). The controlled time-domain responses are compared to the uncontrolled responses, and the effectiveness of PID control, USM optimization, and PSO is compared for this control device configuration. The results of this investigation show PID control to be effective for minimizing both superstructure and substructure responses of highway bridges after high-impact loads. Deck response reductions of greater than 19% and 37% were seen for displacement and acceleration responses, respectively, regardless of the performance index used to analyze them. PSO was much more effective than USM optimization for tuning PID control gains.Item Runtime Estimation of the Number of Active Devices in IEEE 802.15.4 Slotted CSMA/CA Networks with Deferred Transmission and No Acknowledgment Using ARMA Filters(2018) Lee, Won Hyoung; Hwang, Ho Young; Chong, Jo Woon (TTU)We propose a novel method for estimating the number of active devices in an IEEE 802.15.4 network. Here, we consider an IEEE 802.15.4 network with a star topology where active devices transmit data frames using slotted carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol without acknowledgment. In our proposed method, a personal area network (PAN) coordinator of a network counts the number of events that a transmission occurs and the number of events that two consecutive slots are idle in a superframe duration, and the PAN coordinator broadcasts the information through a beacon frame. Each device can count the number of slots that each device is in the backoff procedure and the number of the first clear channel assessment (CCA) that each device performs whenever it performs the first CCA after the backoff procedure. Then, each device estimates the number of active devices in the network based on these counted numbers and the information from PAN coordinator with the help of an autoregressive moving average (ARMA) filter. We evaluate the performance of our proposed ARMA-based estimation method via simulations where active devices transmit data frames in IEEE 802.15.4 slotted CSMA/CA networks. Simulation results show that our proposed method gives estimation errors of the number of active devices less than 4.501% when the actual number of active devices is varying from 5 to 80. We compare our proposed method with the conventional method in terms of the average and standard deviation for the estimated number of active devices. The simulation results show that our proposed estimation method is more accurate than the conventional method.Item Sustainability Transparency and Trustworthiness of Traditional and Blockchain Ecolabels: A Comparison of Generations X and Y Consumers(2021) Navas, Rebekkah (TTU); Chang, Hyo Jung (Julie) (TTU); Khan, Samina (TTU); Chong, Jo Woon (TTU)Consumers and professionals realize the importance of adopting social and environmental responsibility, but it is not easy for companies to implement transparent sustainability strategies that consumers can trust. Thus, it is often hard for consumers to compare brands to make conscious sustainability decisions. Blockchain technology is proposed as a bridge between ecolabels and industry initiatives as this technology provides the transparency of sustainable business practices. The objective of this study is to examine the effects of effectiveness, knowledge of the sustainability initiative, and trust in claims made by a company in ecolabels (i.e., traditional and blockchain ecolabels) on intention to buy products by comparing Generations X and Y. A total of 200 participants completed the survey. The results indicated that both the trust and knowledge measures were higher for the blockchain label than for the traditional ecolabel for Generation Y. Thus, the companies should determine how to effectively integrate this technology to the mutual benefit of the retailer and consumer by different generations.Item User Authentication Recognition Process Using Long Short-Term Memory Model(2022) Ortiz, Bengie L (TTU); Gupta, Vibhuti; Chong, Jo Woon (TTU); Jung, Kwanghee (TTU); Dallas, Tim (TTU)User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.