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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.
Rebekkah Navas; Hyo Chang; Samina Khan; Jo Chong. Sustainability Transparency and Trustworthiness of Traditional and Blockchain Ecolabels: A Comparison of Generations X and Y Consumers. Sustainability 2021, 13, 8469 .
AMA StyleRebekkah Navas, Hyo Chang, Samina Khan, Jo Chong. Sustainability Transparency and Trustworthiness of Traditional and Blockchain Ecolabels: A Comparison of Generations X and Y Consumers. Sustainability. 2021; 13 (15):8469.
Chicago/Turabian StyleRebekkah Navas; Hyo Chang; Samina Khan; Jo Chong. 2021. "Sustainability Transparency and Trustworthiness of Traditional and Blockchain Ecolabels: A Comparison of Generations X and Y Consumers." Sustainability 13, no. 15: 8469.
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.
Kamrul Foysal; Hyo-Jung Chang; Francine Bruess; Jo-Woon Chong. Body Size Measurement Using a Smartphone. Electronics 2021, 10, 1338 .
AMA StyleKamrul Foysal, Hyo-Jung Chang, Francine Bruess, Jo-Woon Chong. Body Size Measurement Using a Smartphone. Electronics. 2021; 10 (11):1338.
Chicago/Turabian StyleKamrul Foysal; Hyo-Jung Chang; Francine Bruess; Jo-Woon Chong. 2021. "Body Size Measurement Using a Smartphone." Electronics 10, no. 11: 1338.
The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market.
Kamrul H. Foysal; Hyo Jung Chang; Francine Bruess; Jo Woon Chong. SmartFit: Smartphone Application for Garment Fit Detection. Electronics 2021, 10, 97 .
AMA StyleKamrul H. Foysal, Hyo Jung Chang, Francine Bruess, Jo Woon Chong. SmartFit: Smartphone Application for Garment Fit Detection. Electronics. 2021; 10 (1):97.
Chicago/Turabian StyleKamrul H. Foysal; Hyo Jung Chang; Francine Bruess; Jo Woon Chong. 2021. "SmartFit: Smartphone Application for Garment Fit Detection." Electronics 10, no. 1: 97.
Francine Bruess; Hyo Jung (Julie) Julie Chang; Jo Woon Chong; Kamrul Foysal. Retail Technologies Leading Resurgence for Small Independent Fashion Retailers: A Thematic Analysis Related to the TOE Framework. Pivoting for the Pandemic 2020, 77, 1 .
AMA StyleFrancine Bruess, Hyo Jung (Julie) Julie Chang, Jo Woon Chong, Kamrul Foysal. Retail Technologies Leading Resurgence for Small Independent Fashion Retailers: A Thematic Analysis Related to the TOE Framework. Pivoting for the Pandemic. 2020; 77 (1):1.
Chicago/Turabian StyleFrancine Bruess; Hyo Jung (Julie) Julie Chang; Jo Woon Chong; Kamrul Foysal. 2020. "Retail Technologies Leading Resurgence for Small Independent Fashion Retailers: A Thematic Analysis Related to the TOE Framework." Pivoting for the Pandemic 77, no. 1: 1.
Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders’ injuries is higher compared to the risk of injuries for motorists. Crashes, riders’ errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this paper, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (X,Y, and Z). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system.
Fatemehsadat Tabei; Behnam Askarian; Jo Woon Chong. Accident Detection System for Bicycle Riders. IEEE Sensors Journal 2020, 21, 878 -885.
AMA StyleFatemehsadat Tabei, Behnam Askarian, Jo Woon Chong. Accident Detection System for Bicycle Riders. IEEE Sensors Journal. 2020; 21 (2):878-885.
Chicago/Turabian StyleFatemehsadat Tabei; Behnam Askarian; Jo Woon Chong. 2020. "Accident Detection System for Bicycle Riders." IEEE Sensors Journal 21, no. 2: 878-885.
As a reliable indicator for individual's healthiness conditions, heart rate (HR) has been widely considered and used. Imaging photoplethysmography (iPPG) is recently highlighted as a promising HR measurement method, due to its non-contact characteristics, by extracting the HR from facial video recordings. In this study, we propose a camera-based HR monitoring technique that estimates HR information from iPPG signals extracted from a video sequence. Videos were recorded using a smartphone or a laptop camera. We adopted the plane-orthogonal-to-skin (POS) method to compute iPPG. The proposed method is evaluated by applying it to extract HR of 9 subjects at rest and during two motion conditions (lateral and frontal) while they were performing several respiratory maneuvers - spontaneous, metronome and forced. Automatic face detection algorithms were implemented in the proposed method. Our experimental results show that mean values of HR have error = 0.56% and accuracy = 99.44% when compared to HR calculated from the gold-standard electrocardiography (ECG) reference in diverse conditions of motions and respiratory maneuvers.
Monay Mokhtar Shoushan; Bersain Alexander Reyes; Aldo Mejia Rodriguez; Jo Woon Chong. Non-Contact HR Monitoring via Smartphone and Webcam During Different Respiratory Maneuvers and Body Movements. IEEE Journal of Biomedical and Health Informatics 2020, 25, 602 -612.
AMA StyleMonay Mokhtar Shoushan, Bersain Alexander Reyes, Aldo Mejia Rodriguez, Jo Woon Chong. Non-Contact HR Monitoring via Smartphone and Webcam During Different Respiratory Maneuvers and Body Movements. IEEE Journal of Biomedical and Health Informatics. 2020; 25 (2):602-612.
Chicago/Turabian StyleMonay Mokhtar Shoushan; Bersain Alexander Reyes; Aldo Mejia Rodriguez; Jo Woon Chong. 2020. "Non-Contact HR Monitoring via Smartphone and Webcam During Different Respiratory Maneuvers and Body Movements." IEEE Journal of Biomedical and Health Informatics 25, no. 2: 602-612.
Recently, smartphones with mobile health applications have become promising tools in the healthcare industry due to their convenience, ubiquity for patients, and the ability to gather data in real time. In this paper, we propose a novel non-invasive, portable, and cuff-less method for monitoring BP by only using the smartphones’ camera. Our experiment uses pulse transit time (PTT) between two separate photoplethysmogram (PPG) signals to estimate the subjects’ systolic blood pressure (SBP) and diastolic blood pressure (DBP). Our proposed method first measures the subject’s PPG signals from his/her index fingers using the smartphones’ camera. Then, filtering and peak detection algorithms of the proposed method reduce the motion and noise artifacts in the PPG signals. Finally, the proposed method estimates SBP and DBP based on a linear regression model which was trained and tested on 30 trials with six healthy subjects. We evaluated the proposed method by comparing BP values of the proposed method with those of the reference (or gold-standard) device in terms of mean absolute error (MAE), standard deviation of error (SD), and R-squared (R2) value of the cross-validation. Experimental results show that the proposed method estimates the average of MAE ± SD is 2.07 ± 2.06 mm Hg for SBP estimation, and 2.12 ± 1.85 mm Hg for DBP estimation. These estimates are lower than accurate BP estimation standard (5 ± 8 mmHg).
Fatemehsadat Tabei; Jon Michael Gresham; Behnam Askarian; Kwanghee Jung; Jo Woon Chong. Cuff-Less Blood Pressure Monitoring System Using Smartphones. IEEE Access 2020, 8, 11534 -11545.
AMA StyleFatemehsadat Tabei, Jon Michael Gresham, Behnam Askarian, Kwanghee Jung, Jo Woon Chong. Cuff-Less Blood Pressure Monitoring System Using Smartphones. IEEE Access. 2020; 8 (99):11534-11545.
Chicago/Turabian StyleFatemehsadat Tabei; Jon Michael Gresham; Behnam Askarian; Kwanghee Jung; Jo Woon Chong. 2020. "Cuff-Less Blood Pressure Monitoring System Using Smartphones." IEEE Access 8, no. 99: 11534-11545.
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.
Kamrul H. Foysal; Sung Eun Seo; Min Ju Kim; Oh Seok Kwon; Jo Woon Chong. Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone. Sensors 2019, 19, 4812 .
AMA StyleKamrul H. Foysal, Sung Eun Seo, Min Ju Kim, Oh Seok Kwon, Jo Woon Chong. Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone. Sensors. 2019; 19 (21):4812.
Chicago/Turabian StyleKamrul H. Foysal; Sung Eun Seo; Min Ju Kim; Oh Seok Kwon; Jo Woon Chong. 2019. "Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone." Sensors 19, no. 21: 4812.
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.
Behnam Askarian; Seung-Chul Yoo; Jo Woon Chong. Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone. Sensors 2019, 19, 3307 .
AMA StyleBehnam Askarian, Seung-Chul Yoo, Jo Woon Chong. Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone. Sensors. 2019; 19 (15):3307.
Chicago/Turabian StyleBehnam Askarian; Seung-Chul Yoo; Jo Woon Chong. 2019. "Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone." Sensors 19, no. 15: 3307.
Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 °C to 5 °C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 °C in underwater environment, while estimation accuracy decreases as temperature goes down.
Behnam Askarian; Kwanghee Jung; Jo Woon Chong. Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments. Sensors 2019, 19, 2846 .
AMA StyleBehnam Askarian, Kwanghee Jung, Jo Woon Chong. Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments. Sensors. 2019; 19 (13):2846.
Chicago/Turabian StyleBehnam Askarian; Kwanghee Jung; Jo Woon Chong. 2019. "Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments." Sensors 19, no. 13: 2846.
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.
Fatemehsadat Tabei; Rifat Zaman; Kamrul H. Foysal; Rajnish Kumar; Yeesock Kim; Jo Woon Chong. A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. PLOS ONE 2019, 14, e0218248 .
AMA StyleFatemehsadat Tabei, Rifat Zaman, Kamrul H. Foysal, Rajnish Kumar, Yeesock Kim, Jo Woon Chong. A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. PLOS ONE. 2019; 14 (6):e0218248.
Chicago/Turabian StyleFatemehsadat Tabei; Rifat Zaman; Kamrul H. Foysal; Rajnish Kumar; Yeesock Kim; Jo Woon Chong. 2019. "A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts." PLOS ONE 14, no. 6: e0218248.
Advancement in nanoscience technology has presented various sensing platform, and the development of biotechnology has found bioprobes for disease diagnostics, leading to nanobiosensors. Although they show high sensitivity and excellent selectivity for target molecules, the nanobiosensors still need additional instruments for consumers in the field. To enable point-of-care testing (POCT) with nanobiosensors, smart devices, consisting of portable memory storage, a light source, electrical boards, a battery and so on, have been combined with nanobiosensors. Smartphone-based nanobiosensors showed extended functions with optical, physical and electrochemical modulators, leading to an all-in-one sensing system. In this review, we introduce various nanobiosensors and their integration with smartphones. Moreover, an excellent demonstration of smartphone-based nanobiosensors is presented in each chapter with several subjects: i) optical, ii) physical and iii) electrochemical nanobiosensors on smartphones.
Sung Eun Seo; Fatemehsadat Tabei; Seon Joo Park; Behnam Askarian; Kyung Ho Kim; Golanz Moallem; Jo Woon Chong; Oh Seok Kwon. Smartphone with optical, physical, and electrochemical nanobiosensors. Journal of Industrial and Engineering Chemistry 2019, 77, 1 -11.
AMA StyleSung Eun Seo, Fatemehsadat Tabei, Seon Joo Park, Behnam Askarian, Kyung Ho Kim, Golanz Moallem, Jo Woon Chong, Oh Seok Kwon. Smartphone with optical, physical, and electrochemical nanobiosensors. Journal of Industrial and Engineering Chemistry. 2019; 77 ():1-11.
Chicago/Turabian StyleSung Eun Seo; Fatemehsadat Tabei; Seon Joo Park; Behnam Askarian; Kyung Ho Kim; Golanz Moallem; Jo Woon Chong; Oh Seok Kwon. 2019. "Smartphone with optical, physical, and electrochemical nanobiosensors." Journal of Industrial and Engineering Chemistry 77, no. : 1-11.
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.
Won Hyoung Lee; Ho Young Hwang; Jo Woon Chong. 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. Wireless Communications and Mobile Computing 2018, 2018, 1 -12.
AMA StyleWon Hyoung Lee, Ho Young Hwang, Jo Woon Chong. 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. Wireless Communications and Mobile Computing. 2018; 2018 ():1-12.
Chicago/Turabian StyleWon Hyoung Lee; Ho Young Hwang; Jo Woon Chong. 2018. "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." Wireless Communications and Mobile Computing 2018, no. : 1-12.
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.
Rifat Zaman; Chae Ho Cho; Konrad Hartmann-Vaccarezza; Tra Phan; Gwonchan Yoon; Jo Woon Chong. Novel Fingertip Image-Based Heart Rate Detection Methods for a Smartphone. Sensors 2017, 17, 358 .
AMA StyleRifat Zaman, Chae Ho Cho, Konrad Hartmann-Vaccarezza, Tra Phan, Gwonchan Yoon, Jo Woon Chong. Novel Fingertip Image-Based Heart Rate Detection Methods for a Smartphone. Sensors. 2017; 17 (2):358.
Chicago/Turabian StyleRifat Zaman; Chae Ho Cho; Konrad Hartmann-Vaccarezza; Tra Phan; Gwonchan Yoon; Jo Woon Chong. 2017. "Novel Fingertip Image-Based Heart Rate Detection Methods for a Smartphone." Sensors 17, no. 2: 358.