Smartphone System Research Articles

Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo

Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detection of different cancer types and highlights the advancements of AI for in vivo cancer detection in smartphone-based imaging. However, these compact smartphone systems face challenges like low imaging quality and restricted computing power. The use of advanced AI algorithms to address the optical and computational limitations of smartphone-based imaging systems provides promising solutions. AI-based cancer detection also faces challenges. Transparency and reliability are critical factors in gaining the trust and acceptance of AI algorithms for clinical application, explainable and uncertainty-aware AI breaks the black box and will shape the future AI development in early cancer detection. The challenges and solutions for improving AI accuracy, transparency, and reliability are general issues in AI applications, the AI technologies, limitations, and potentials discussed in this paper are applicable to a wide range of biomedical imaging diagnostics beyond smartphones or cancer-specific applications. Smartphone-based multimodal imaging systems and deep learning algorithms for multimodal data analysis are also growing trends, as this approach can provide comprehensive information about the tissue being examined. Future opportunities and perspectives of AI-integrated smartphone imaging systems will be to make cutting-edge diagnostic tools more affordable and accessible, ultimately enabling early cancer detection for a broader population.

Thiourea enhanced oxidase-like activity of CeO2/CuxO nanozyme for fluorescence/colorimetric detection of thiourea and glutathione

A novel fluorescence/colorimetric dual-mode sensor, based on enhancement of the oxidase-like activity of CeO2/CuxO nanozyme towards the oxidation of o-phenylenediamine (OPD) induced by thiourea (TU), has been proposed for TU detection. The catalytic activity enhancement on CeO2/CuxO can be attributed to the strong electron-donation ability of TU, which promoted hydroxyl radical generation and amplified OPD oxidization with enhanced dual-signal readout. By integrating a portable paper-chip and smartphone system, this CeO2/CuxO-OPD system achieved on-site visual colorimetric analysis of TU. The dual-mode sensor demonstrated high sensitivity and specificity in recognizing TU, with a detection limit (LOD) of 1.90 μM and a linear range (LR) 2.5–80 μM in fluorescent mode; as well as an LOD of 6.69 μM and an LR 10–250 μM in colorimetric mode. Furthermore, the CeO2/CuxO-TU-OPD system has been designed for dual-mode glutathione (GSH) detection with enhanced sensitivity, achieving an LOD of 0.19 μM and an LR 0.5–10 μM in fluorescent mode; as well as an LOD of 1.24 μM and an LR 1.25–25 μM in colorimetric mode. Additionally, GSH discrimination (fluorescent mode) was successfully achieved in different biological samples, showing good consistency with the standard method. The recoveries ranged from 96.8 % to 116.7 % in serum samples and from 97.3 % to 107.7 % in cell lysates, with RSDs less than 2 %. This work not only introduced a novel approach to enhance oxidase-like activity of nanozymes but also provided an efficient field-suitable tool for enhanced dual-mode response towards TU and GSH.

A ‘‘Turn-on’’ Al(III)-Mediated Fluorescent Micro-Probe for Quercetin Sensing

Quercetin (Qr) is a common flavonoid widely found in plants with promising pharmacological activities. It is thus worthwhile to design a convenient method for its detection. In this work, a specific Al3+-regulated poly(cyclotriphosphazene-co-curcumin) (PC-Al) micro-probe was designed for real-time detection of Qr, therein significant “turn-on” fluorescence was observed after addition of Qr to PC-Al. Under optimal conditions, the probe has a detection limit of 23.2 nM for Qr sensing with good linearity in the range of 0–5 × 10−5 M. Moreover, real-time applications of the PC-Al probe in red wine, orange juice, and honey samples were validated. Furthermore, assisted by a consumer smartphone sensing platform, the sensor shows an excellent linear response between the red/blue ratio (R/B) of the two color channels in the picture and the concentration of Qr in the range of 1–120 μM, achieving quantitative detection of Qr. The introduction of smartphone system to realize convenient detection of Qr demonstrates good application prospects of this Al3+-mediated fluorescence probe.

Multi-deformable interpenetrating network thermosensitive hydrogel fluorescent device for real-time and visual detection of nitrite

Functionalized thermosensitive hydrogel materials exhibit excellent properties for the fabrication of sensing devices that enable real-time visual detection of food safety duo to their good plasticity and powerful loading capacity. Here, a ratiometric fluorescent device based on an interpenetrating network (IPN) thermosensitive hydrogel was designed to embed functionalized Au nanoclusters (Au NCs) and Blue Carbon dots (BCDs) composites in a multi-network structure to build a sensitive hazardous material nitrite (NO2-) chemsensor. The hydrogel was utilized poloxamer 407 (P407), lignin and cellulose to form stable IPN structure, which resulted in complementation and synergy, thereby strengthening its porous network structure. The combination of fluorescent nanoprobes with the porous network structure has the potential to enhance stable fluorescence signals and improve sensing sensitivity. Moreover, the thermosensitive liquid-solid transition characteristics of the hydrogel facilitate its preparation into diverse sensing devices following curing at room temperature. The hydrogel device, when combined with a smartphone system, converted image information into data information, thereby enabling the accurate quantification of NO2- with a detection limit of 9.38 nM in 2 s. The designed multi-functional hydrogel device is capable of real-time differentiation of NO2- dosage with the naked eye, offering a high-contrast, rapid-response sensing methodology for visual assessment of food freshness. This research contributes to the expansion of hydrogel materials applications and the detection of hazardous materials in food safety.

Text Entry Performance and Situation Awareness of a Joint Optical See-Through Head-Mounted Display and Smartphone System.

Optical see-through head-mounted displays (OST HMDs) are a popular output medium for mobile Augmented Reality (AR) applications. To date, they lack efficient text entry techniques. Smartphones are a major text entry medium in mobile contexts but attentional demands can contribute to accidents while typing on the go. Mobile multi-display ecologies, such as combined OST HMD-smartphone systems, promise performance and situation awareness benefits over single-device use. We study the joint performance of text entry on mobile phones with text output on optical see-through head-mounted displays. A series of five experiments with a total of 86 participants indicate that, as of today, the challenges in such a joint interactive system outweigh the potential benefits.

Sensitive and reliable hybrid nanosensor (Co2+-CDs@R-CDs) for ratiometric fluorescent and colorimetric detecting NO2−

A ratiometric fluorescent and colorimetric detecting assay for NO2− was realized by a hybrid nanosensor (Co2+-CDs@R-CDs) utilizing firstly through the redox reaction of nitrite (NO2−) with Co2+, of which the hybrid nanosensor Co2+-CDs@R-CDs was fabricated by Co2+-doped carbon dots (Co2+-CDs) and a reference of red-emitting carbon dots (R-CDs). The ratiometric fluorescent linear detection range of NO2− was 2.5–45 μM and the limit of detection (LOD) was 0.068 μM with the response time of 120 s. While, the colorimetric linear detection range of NO2− was 2.5–60 μM and the LOD was 0.075 μM. In addition, a portable smartphone system which could measure the R (red), G (green), and B (blue) values of the fluorescence and the visible color of the coated Co2+-CDs@R-CDs paper strip-based sensor had also been developed and successfully applied to detect NO2− in sausage, pickles and tap water samples.

Assessing Spatiotemporal Behavior of Human Gait: A Comparative Study Between Low-Cost Smartphone-Based Mocap and OptiTrack Systems

AbstractThis study assessed the accuracy of a low-cost marker-based motion capture system with smartphone devices to estimate the spatiotemporal behavior of human gait in comparison with the performance of the commercial OptiTrack system. Initially, three test subjects were selected for the study, and after equipping them with passive retroreflective markers, they were recorded for gait velocities of 1.50, 1.90, and 2.30 $$m\bullet {s}^{-1}$$ m ∙ s - 1 while collecting kinematic data and videos. The results showed that the smartphone motion capture system exhibited significant spatiotemporal tracking and accuracy in the x-y trajectories and estimation of joint relative angles of the hip, knee, and ankle joints (θ1, θ2, and θ3, respectively) compared to the commercial OptiTrack system. In this comparison, an average goodness-of-FIT and normalized root mean square error of over 88.93% and 2.71% were obtained, respectively, for the joint relative angles of the hip and knee (θ1 and θ2) in all tests performed. However, the accuracy of the joint relative angle of the ankle (θ3, average FIT: 71.04% and nRMSE: 4.26%) was lower because of the low capture rate of the retroreflective markers in the smartphone system and the higher relative velocity in the lower extremities of the test subjects, which generated noise in the calculation of x-y trajectories. This decrease in accuracy has been reported in other studies. However, both motion capture systems experienced marker data loss at the hip, highlighting the need for improvement in the spatial distribution of the optical devices. The OptiTrack system demonstrated better optical redundancy but still required improvements. In contrast, the smartphone system, with its inherent limitations in terms of optical redundancy and spatial distribution, can be enhanced by incorporating multiple cameras for a three-dimensional view. Despite these limitations, the low-cost smartphone system showed optimal performance with minimal errors compared with the commercial system, making it a cost-effective option with potential for further development. The rapid advancement of smartphone technology and its accessibility make it an attractive choice for motion capture applications.

Double-Network structure chemically programmed DNA Cryogel: Enzyme immobilization platform with enhanced catalytic activity for portable biosensing

Enzyme-based hydrogels have conferred multiple benefits, particularly for disease diagnosis and environmental monitoring due to their pronounced biocompatibility, ability to load cargo, and optical properties. However, traditional hydrogels suffer from some significant limitations, such as enzyme leakage and catalytic activity decreased, restricting the extent to which hydrogels can be beneficially used. Herein, chemically programmed DNA-DNA interactions were designed as a promising solution for enzyme immobilization that substantially maintains biological functionality with remarkable leakage reduction. Additionally, their responsiveness and mechanical properties are dramatically enhanced after adjusting the porous structure of hydrogels via freezing. As a proof-of-concept, a DNA cryogel with a hierarchical structure was developed with adjustable distance among cascade enzymes, increasing the local concentration of intermediates and reducing their mass transfer energy barriers for direct glucose monitoring and glutathione (GSH) sensing. Moreover, after being integrated with a smartphone system, a universal sensing platform (immunosensor) was fabricated for rapid detection of environmental chemicals (i.e., bisphenol A) without expensive instruments and sophisticated procedures. This strategy provided some novel insights on hydrogel- based bioassays, implying their great potentials for conducting onsite analysis against various targets.

Innovative solutions to support individuals with disabilities accessing public transportation: A case study

ABSTRACT Individuals with cognitive disabilities have challenges with personal navigation and wayfinding, especially when traveling on public transportation. The purpose of this case study is to describe the structure and implementation of the Personal Navigation for Individuals with Disabilities (PNID) education and training program, which is based on a socio-technical architecture for individuals with cognitive disabilities within a fixed-route public bus system. A case study methodology was used to describe preliminary findings of the skills, attributes, and experiences of three individuals with cognitive disabilities as it relates to transportation on fixed-route bus systems in a midsized urban setting. The three individuals completed five training activities: safety, public bus, smartphone, WayFinder App, and fixed-route bus system. The case study provided a preliminary mixed-methods overview of training travelers with cognitive disabilities to use the WayFinder system while accessing fixed-route public bus system. The insights and strategies identified through the case study demonstrate the potential opportunities for development, implementation, and sustainability of the PNID program in other midsized urban settings. The PNID program (i.e. AT service delivery process), in combination with the WayFinder system (i.e. assistive technology), has the potential to meet the unique needs of individuals with cognitive disabilities when accessing public transportation.

Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor.

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.

Validation of Linear and Nonlinear Gait Variability Measures Derived From a Smartphone System Compared to a Gold-Standard Footswitch System During Overground Walking.

Smartphones, with embedded accelerometers, may be a viable method to monitor gait variability in the free-living environment. However, measurements estimated using smartphones must first be compared to known quantities to ensure validity. This study assessed the validity and reliability of smartphone-derived gait measures compared to a gold-standard footswitch system during overground walking. Seventeen adults completed three 8-minute overground walking trials during 3 separate visits. The stride time series was calculated as the time difference between consecutive right heel contact events within the footswitch and smartphone-accelerometry signals. Linear (average stride time, stride time standard deviation, and stride time coefficient of variation) and nonlinear (fractal scaling index, approximate entropy, and sample entropy) measures were calculated for each stride time series. Bland-Altman plots with 95% limits of agreement assessed agreement between systems. Intraclass correlation coefficients assessed reliability across visits. Bland-Altman plots revealed acceptable limits of agreement for all measures. Intraclass correlation coefficients revealed good-to-excellent reliability for both systems, except for fractal scaling index, which was moderate. The smartphone system is a valid method and performs similarly to gold-standard research equipment. These findings suggest the development and implementation of an inexpensive, easy-to-use, and ubiquitous telehealth instrument that may replace traditional laboratory equipment for use in the free-living environment.

DYADIC HEALTH SCIENCE IN GERONTOLOGY: RECENT ADVANCES AND INTERDISCIPLINARY PERSPECTIVES

Abstract Close others impact health in profound ways, and many of the critical pathways can only be understood by examining the dynamics that emerge between two people. This symposium will present new theoretical and methodological advances in dyadic health science, and invite discussion of differing perspectives on the scope of dyadic research. Dr. Wilson will overview the essential components of rigorous dyadic health research and explain the range of approaches used to define dyadic research, setting the stage for the cutting-edge work described in the subsequent presentations. Drs. Novak and Yorgason will discuss the importance of matching research questions with dyadic data and how to best model congruence/similarity, difference scores, accuracy or bias, and idealization between partners. Dr. Pauly will describe a recently proposed developmental-contextual model of couple synchrony, and present findings from research using a smartwatch and smartphone system (DyMand) that captures dyadic interactions. Dr. Polenick will present findings from research examining effects of incongruent and discordant health conditions within older couples on depressive symptoms. Dr. Christine Proulx, an internationally recognized scholar of close relationships in late life, will offer perspective on these advances and critical future directions for the field. This is a Dyadic Research on Health and Illness Across the Adult Lifespan Interest Group Sponsored Symposium.

Dual-channel MIRECL portable devices with impedance effect coupled smartphone and machine learning system for tyramine identification and quantification.

We designed a novel, portable, and visual dual-potential molecularly imprinted ratiometric electrochemiluminescence (MIRECL) sensor for tyramine (TYM) detection based on smartphone and deep learning-assisted optical devices. Molecularly imprinted polymer-Ce2Sn2O7 (MIP-Ce2Sn2O7) layers were fabricated by in-situ electropolymerization method as the capture and signal amplification probe. Oxygen vacancies in Ce2Sn2O7 not only enhance the electrochemical redox capability but also accelerate the energy transfer, thereby enhancing the luminescence of cathode ECL. Under optimal conditions, the ECL signals of MIP-Ce2Sn2O7 at the cathode and the anode response of Ru(bpy)32+ was reduced, thus a wide linear range from 0.01μM to 1000μM with the detection limit as low as 0.005μM. Interestingly, combined with an artificial intelligence image recognition algorithm and the principle of optical signal reading by smartphone, the developed MIRECL sensor has been applied to the portable and visual determination of TYM in aquatic samples, and its practicability has been satisfactorily verified.

Wellbeing, loneliness, health-related quality of life and perception of technology of older adults in Slovenian senior homes: a cross-sectional study

Background: Older adults can experience loneliness, low wellbeing, and poor health, while certain technologies could alleviate some of these issues. This paper presents findings from a study conducted in Slovenian senior homes, examining the wellbeing, loneliness, health-related quality of life, and perceptions of technology among older adults without significant cognitive issues. Methods: Older adults (n = 73) responded to questionnaires on wellbeing, loneliness, and health-related quality of life. They also provided their opinion on four different types of technology: a smartphone (SeniorsPhone) and television-based video calling system adapted to older adults (Daisy), a smartwatch (Fitbit), and an air quality sensor. Results: The results indicate that most examined older adults experience low or moderate levels of loneliness, and most are characterized by average or high levels of wellbeing. The participants also report no significant issues with self-care and accomplishing daily activities. However, more than half of the participants report experiencing some problems with pain/discomfort and mobility. Older adults generally trust evaluated technologies with their personal data, believe that the technologies provide reliable information, and perceive technologies as useful. Interestingly, older adults that reported higher levels of wellbeing and lower levels of loneliness tended to perceive the smartwatch as more usable. This suggests that at least for some technologies, the perception of usability depends on how people feel in general. Conclusions: Residents of Slovenian senior homes that do not have significant cognitive issues report relatively high wellbeing and health-related quality of life and low loneliness, together with positive perceptions of different technologies. These findings can be used to inform the development of age-appropriate technologies for senior home residents.

An AI based smart-phone system for asbestos identification

Asbestos identification is a complex environmental and economic challenge. Typical commercial identification of asbestos involves sending samples to a laboratory where someone learned in the field uses light microscopy and specialized mounting to identify the morphologically distinct signatures of Asbestos. In this work we investigate the use of a portable (30x) microscope which works with a smart phone camera to develop an image recognition system. 7328 images from over 1000 distinct samples of cement sheet from Melbourne, Australia were used to train a phone-based image recognition system for Asbestos identification. Three common CNN’s were tested ResNet101, InceptionV3 and VGG_16 with ResNet101 achieving the best result. The distinctiveness of Asbestos was found to be identified correctly 90% of the time using a phone-based system and no specialized mounting. The image recognition system was trained with ResNet101 a convolutional neural network deep learning model which weights layers with a residual function. Resulting in an accuracy of 98.46% and loss of 3.8% ResNet101 was found to produce a more accurate model for this use-case than other deep learning neural networks.

Information Extraction for Touch Free Biometric Authentication System from Large Databases using Deep Learning Algorithm

Users' privacy must be strictly protected because smartphones have become an essential tool for storing sensitive data. This may be performed by using the most accurate and trustworthy bio-metric authentication techniques available, like iris, voice, facial and fingerprints cognizance. This paper presents a way to develop and test biometric recognition system for smartphones. Touch-less biometric recognition system is an implementation of biometric verification and identification system on mobile phones that utilizes mobile phone cameras and microphone to acquire the biometrics of the user. Once the data is captured, they are pre-processed using standard pre-processing algorithm to obtain the skeleton. This is used to extract true minutiae eliminating all the false minutiae. The usage of mobile phones for biometric recognition makes it quite convenient and feasible for everyone. Also, touch-less system has a lot of advantages over touch-based ones and hence, they become a more popular choice.

Analisis Bukti Digital Direct Message Pada Twitter Menggunakan Metode National Institute Of Justice (NIJ)

Twitter is a social media application that can be used on desktop, web and smartphone systems. The large number of Twitter users makes Twitter inseparable from cyber crimes, such as pornography, online gambling, hate speech and other crimes. The purpose of this study is to conduct a forensic investigation on the Twitter Direct Message (DM) application using the NIJ method and determine the success rate of forensic tools in finding deleted data. This study uses the National Institute of Justice (NIJ) method with the stages of identification, collection, examination, analysis, and reporting assisted by forensic tools to obtain digital traces that can be used as evidence. The results obtained from this study were the discovery of evidence of deleted chats and images on Twitter DMs with a success rate of forensic tools (Magnet AXIOM and MOBILEedit) in finding these evidences of 66.66%.

Securing Virtual Architecture of Smartphones based on Network Function Virtualization

One of the most difficult parts of Network Function Virtualization (NFV) installations is security. The NFV environment is a large-scale, software-driven one with a variety of components. Network topologies and traffic flows are continuously and managed to change. Such complexity necessitates a comprehensive security framework that permits automatic and to manage changeable network conditions, a quick response is required with the least amount of manual involvement. This paper introduced many solutions for securing the NFV environment from attacks such as (Specter and DoS) that attack parts of this architecture based on some experiments. Applied NFV on an operating system of smartphones (Android). We tested some attacks on the device and then on some of the layers in the architecture. We obtain new and obvious results, by comparison, to traditional and updated NFV architecture. Also, update the NFV architecture using vCenter/ESX and Hyper-V being two important terms in security After adding the necessary algorithms to protect the NFV architecture, we noticed about 128 hours to hack a 1,4 megabyte (WinRAR) file, while the same file and the same size needed 126 hours to reach the root without the algorithms used to protect the architecture.

Validation and implementation of a mobile app decision support system for prostate cancer to improve quality of tumor boards.

Certified Cancer Centers must present all patients in multidisciplinary tumor boards (MTB), including standard cases with well-established treatment strategies. Too many standard cases can absorb much of the available time, which can be unfavorable for the discussion of complex cases. In any case, this leads to a high quantity, but not necessarily a high quality of tumor boards. Our aim was to develop a partially algorithm-driven decision support system (DSS) for smart phones to provide evidence-based recommendations for first-line therapy of common urological cancers. To assure quality, we compared each single digital decision with recommendations of an experienced MTB and obtained the concordance.1873 prostate cancer patients presented in the MTB of the urological department of the University Hospital of Cologne from 2014 to 2018 have been evaluated. Patient characteristics included age, disease stage, Gleason Score, PSA and previous therapies. The questions addressed to MTB were again answered using DSS. All blinded pairs of answers were assessed for discrepancies by independent reviewers. Overall concordance rate was 99.1% (1856/1873). Stage specific concordance rates were 97.4% (stage I), 99.2% (stage II), 100% (stage III), and 99.2% (stage IV). Quality of concordance were independent of age and risk profile. The reliability of any DSS is the key feature before implementation in clinical routine. Although our system appears to provide this safety, we are now performing cross-validation with several clinics to further increase decision quality and avoid potential clinic bias.

Predicting intention to use QR code mobile payment among Malaysian Muslim millennials

Financial transactions in the digital age have shifted from traditional to digital systems. With the proliferation of smartphones and the internet, cashless payments via smartphone or mobile payment system are becoming more popular. Quick Response (QR) code mobile payment is one of the cashless payment options available today. Using the extended theory of Technology Acceptance Model (TAM), this study investigates the intention of Malaysian Muslim millennials to opt for QR code mobile payment. The study was conducted as a cross-sectional study with a convenience sample. Data was collected through a questionnaire distributed via Google forms on various social media applications. Multiple linear regression analysis was used to analyse the data. The results showed that perceived usefulness, perceived risk, hedonic motivation, and performance expectancy have a significant influence on the intention of Malaysian Muslim millennials to use QR code mobile payment as a payment method. Perceived ease of use and social influence, on the other hand, were not predictors of intention to use QR code mobile payment. This study contributes to filling the gap in the literature on technology acceptance, digital payments, and consumer behaviour. At the same time, this study can serve as a basis for QR code developers and retailers to better understand and serve their customers, and for the government to integrate more QR code applications into many government-provided services.