Smartphone with optical, physical, and electrochemical nanobiosensors

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Foysal

Seo

Kim

et al. 2019

Self Cite

“…There have been studies investigating the effect of different conditions and factors including motion noise artifact, skin tone, nail polishes, age, and ambient light on PPG signal amplitude [8,9,10,11]. For heart rate measurements in underwater condition, several approaches have been proposed [10,11]. Schipke et al found that 5 min immersions for divers in 6 °C cold ocean water resulted in 10% decrease in SpO 2 measurement accuracy and 40% decrease in heart rate measurement accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, continuous heart rate monitoring is highly demanded to prevent the complications of CVDs. Recently, smartphone applications for healthcare, e.g., mobile heart disease detection, heart rhythm analysis, remote home care monitoring, eye disease diagnosis, have become highlighted [4,5,6,7,8,9,10]. Moreover, smartwatches, wrist bands, and activity trackers based on electrocardiography (ECG) or photoplethysmography (PPG) are also widely used for continuous measurement of physiological signs such as heart rate and peripheral oxygen saturation (SpO 2 ) in daily life [11].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments

Askarian

Jung

Chong

2019

Self Cite

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.

“…The results are obtained through a smartphone application, which reads the colorimetric results from the strip and provides a positive or negative result based on a cutoff threshold. This biosensing approach has been the subject of several papers, where tools for registration, transfer, and analysis of assays data were discussed [93][94][95][96]. A summary of the patient details and a description of the test taken are given in Figure 9B.…”

Section: Microfluidic-based Ehealthmentioning

confidence: 99%

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Mejía‐Salazar

Cruz

Vásques

et al. 2020

145

111

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.