Smartphone System Research Articles

Implementation of Smartphone Systems to Improve Quality of Life for People With Substance Use Disorder: Interim Report on a Randomized Controlled Trial.

BackgroundResearchers have conducted numerous studies seeking to understand how to improve the implementation of changes in health care organizations, but less focus has been given to applying lessons already learned from implementation science. Finding innovative ways to apply these findings efficiently and consistently will improve current research on implementation strategies and allow organizations utilizing these techniques to make changes more effectively.ObjectiveThis research aims to compare a practical implementation approach that uses principles from prior implementation studies to more traditional ways of implementing change.MethodsA total of 43 addiction treatment sites in Iowa were randomly assigned to 2 different implementation strategies in a randomized comparative effectiveness trial studying the implementation of an eHealth substance use disorder treatment technology. One strategy used an adaptation of the Network for the Improvement of Addiction Treatment (NIATx) improvement approach, while the other used a traditional product training model. This paper discusses lessons learned about implementation.ResultsThis midterm report indicates that use of the NIATx approach appears to be leading to improved outcomes on several measures, including initial and sustained use of new technology by both counselors and patients. Additionally, this research indicates that seamlessly integrating organizational changes into existing workflows and using coaching to overcome hurdles and assess progress are important to improve implementation projects.ConclusionsAt this interim point in the study, it appears that the use of the NIATx improvement process leads to better outcomes in implementation of changes within health care organizations. Moreover, some strategies used in this improvement process are particularly useful and should be drawn on more heavily in future implementation efforts.Trial RegistrationClinicalTrials.gov NCT03954184; https://clinicaltrials.gov/ct2/show/NCT03954184

Equity and exclusion issues in cashless fare payment systems for public transportation

Many transit agencies in the United States plan to automate their fare collection and limit–or even eliminate–the use of cash fares, with the goals of expediting boarding, collecting data, and lowering costs. Yet about 10% of US adults lack a bank account or credit card, and many rely on restrictive cellphone data plans or do not have access to the internet or a smartphone. These riders will find it difficult to access transit in the future. This paper examines transit users’ experiences with fare technologies using a survey of riders in three cities. Our analysis reveals which riders are most at risk of being excluded, and how mitigation strategies could work to overcome barriers to cash-less transit. We find that a significant number of riders (∼30%) currently use cash on-board buses. If on-board cash fares were to be removed, a significant share of these riders appear able to switch to other options, though many imagine they will continue to use cash in some way (e.g. at retail or ticket vending machines); a small number claim they would no longer be able to ride transit if on-board cash fares were removed. Older and lower-income riders are more at risk of exclusion as they often lack access to smartphones or the internet. A significant number rely on less dependable internet sources, such as public Wi-Fi, potentially inhibiting some from using smartphone and internet-based payment systems. Findings suggest approaches to reduce the number of riders excluded from transit during fare technology adoption.

291-OR: Using the Performance in Exercise and Knowledge (PEAK) Guidelines Incorporated in a Smartphone-Based Decision Support System Improves Glucose Outcomes during Free-Living Exercise

DailyDose is a smart-phone decision support system developed at Oregon Health & Science University that uses Dexcom G6 CGM and Medtonic’s InPen. The app calculates insulin doses using CGM value and trend, insulin-on-board, carbohydrate amount, and exercise information. Insulin dosing and carbohydrate intake recommendations before and after exercise are adjusted according to the 2017 consensus statement by Riddell et al. known as the PEAK Guidelines. Twenty-four adults with T1D, using multiple daily injections of insulin at baseline, completed a two-week run-in period then used the DailyDose intervention for 8 weeks. Participants completed 3 at-home exercise sessions per week, one aerobic exercise video and two other sessions of their choice. We examined the impact of the use of PEAK guidelines on glucose outcomes during exercise comparing 176 exercise sessions done during run-in and 471 sessions done during the intervention period. Glucose outcomes were assessed from start of exercise to 4 hours after the start or until either a meal was consumed, insulin dosed or new exercise session initiated. Mixed effects analysis was used to determine significance of the intervention on glucose outcomes. The nadir of the glucose was lower for the run-in compared with the intervention (120.6 vs. 130.9 mg/dL, P=.012) . Change in glucose from the start of exercise to the nadir was 58.0 for the run-in versus 46.9 for the intervention period (P=.016) . Time in hyperglycemia (>180mg/dL) during the exercise periods trended toward being lower during the intervention (43.8% run-in, 35.0% intervention, P=.059) , as did time in range of 70-180 mg/dL, (54.2% run-in, 62.2% intervention, P=.081) . There was no difference in time in hypoglycemia. Data suggest that use of PEAK within a decision support app can help prevent more severe glucose drops during and after exercise with a trend toward improving time in range. Disclosure L.M.Wilson: n/a. S.M.Oganessian: None. D.Branigan: None. V.Gabo: None. J.H.Eom: None. J.El youssef: None. K.Winters-stone: None. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. F.H.Guillot: Stock/Shareholder; Pacific Diabetes Technologies. N.S.Tyler: None. T.Kushner: Consultant; Tandem Diabetes Care, Inc. A.Z.Espinoza: None. C.M.Mosquera-lopez: None. J.Pinsonault: None. R.Dodier: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (Grant 2018PG-T1D001) , OHSU Medical Research Foundation, Supplies provided by Dexcom

The health economic effects of an imaging technology-based telemedicine system for rural neuro-emergency patient care.

"Join," an imaging technology-based telemedicine system, allows simultaneous radiological information sharing between physically remote institutions, virtually connecting advanced medical institutions and rural hospitals. This study aimed to elucidate the health economics effect of Join for neurological telemedicine in rural areas in Hokkaido, Japan. Information concerning 189 requests for patient transfer from Furano Kyokai Hospital, a regional rural hospital, to Asahikawa Medical University Hospital (AMUH), an advanced academic medical institution, was retrospectively collected. The Join system was established between Furano Kyokai Hospital and AMUH in February 2019. Data collected from patients between April 2017 and December 2018 were included in the non-Join group, and those collected between February 2019 and October 2020 were included in the Join group. Clinical variables, reasons for patient transfer requests, duration of hospital stay, and medical costs per patient were analyzed between these two groups. Furthermore, clinical characteristics were compared between patients who were transferred and not transferred based on Join. More patients were discharged < 7 days after transfer to AMUH in the non-Join group compared with the Join group (p = 0.02). When focusing on the Join group, more patients who were not transferred were discharged < 1 week (p < 0.01). On the other hand, more patients required surgery (p = 0.01) when transferred. The ratio of patients whose medical cost was < USD5000 substantially decreased, from 33% for the non-Join group to 13% for the Join group. An imaging technology-based telemedicine system, Join, contributed to reducing unnecessary neuro-emergency patient transfer in a remote rural area, and telemedicine with an integrated smartphone system allowed medical personnel to effectively triage at a distance neuro-emergency patients requiring advanced tertiary care.

Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

Android is one of the leading operating systems for smartphones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing the Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts’ intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for the detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce (1) a novel technique for opcodes embedding, which we name Op2Vec, and (2) based on the learned Op2Vec, we have developed a dataset for end-to-end detection of Android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted feature extraction and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976, and F1 score of 0.979.

High-Efficiency Encryption and Authentication Network Security for Automotive Ethernet

Automotive ethernet has the advantages of high bandwidth, low latency, and strong compatibility, which meets the needs of new energy vehicles for the development of network integration. Automotive ethernet can not only solve the problem of increased wiring harness and complicated wiring in the intelligentization of automotive electronics, but also can improve the comfort, reliability and multiple safety of the car. Although the car is connected to the smart phone, Bluetooth, Internet and other network systems to improve the driving pleasure for the driver, but it brings hacker attacks, security loopholes and other car network security problems that cannot be ignored, which seriously affects the safe driving of the car, personal privacy, and even endanger public safety. In this paper, we focus on the need for the network security of automotive ethernet, and analyses problem in encryption authentication algorithm. An improved AES-128 encryption algorithm and an improved MD5 authentication algorithm are proposed innovatively. Through the experimental simulation of CANoe.Ethernet, the improved AES-128 encryption algorithm proposed in this paper is 15% more efficient than the traditional encryption algorithm, and the improved MD5 authentication algorithm is 4 times faster than the traditional authentication algorithm. Thus, the active network security performance of automotive ethernet is further improved.

Monitoring and Controlling of LAN Through Android Application for Network Security

Abstract: Mobile devices are becoming essential for today's life. In developed countries, about half of the people have a Smartphone, resulting in millions of these electronic devices. Android is the most popular operating system for Smartphones and other electronic devices such as tablets. Hence, for network administrators, it is essential to start managing all the Android based devices. Network is formed by the collection or group of computers. While in office is an easy task to manage and control the activities of the network. A remote LAN (Local Area Network) network can be controlled by a user from anywhere using a mobile based application (running on ANDROID operating system). The necessity of such a system arises when the user in unavailable at the actual site of the network. In such a case, to monitor and control the various activities of the network, a wireless and user friendly interface needs to be created through which the user can execute different commands to control the various activities of the network by using Wi-Fi, GPRS. Keywords: Android, remote LAN, Wi-Fi, GPRS.

Uni-Planar MIMO Antenna for Sub-6 GHz 5G Mobile Phone Applications

This article presents the design of a uni-planar MIMO antenna system for sub-6 GHz 5G-enabled smartphones. The MIMO antenna designed comprises four loop-shaped radiators placed at each corner of the mobile phone board, which follows the principle of pattern diversity. The single-antenna element resonates at 3.5 GHz, its impedance bandwidth is noted to be 1.28 GHz (3–4.28 GHz) for S11≤ −6 dB, and it is equal to 720 MHz (3.18–3.9 GHz) for S11≤ −10 dB. For a single-antenna element, a peak gain of 3.64 dBi is observed with an antenna efficiency of &gt;90%. The isolation of &gt;10 dB between antenna elements is achieved for the MIMO configuration. Furthermore, the MIMO antenna designed provides enough radiation coverage to support different sides of the mobile phone board, which is an important feature for future 5G-enabled handsets. In addition, the impacts of human hands and heads on MIMO antenna performance are investigated, and acceptable performance in the data and conversation modes is observed.

What Is the Difference? Investigating the Self-Report of Wellbeing via Conversational Agent and Web App

Adopting speech as their mode of interaction, speech-enabled conversational agent (CA) systems hold the potential to enable more natural and engaging self-report experiences than traditional media (e.g., pen and paper, web, or smartphone systems). Our recent research concerns the potential design and use of CAs to support mental health and wellbeing. In this article, we present findings from a study during which 22 individuals with affective disorders (e.g., depression, bipolar disorder) used either a speech-enabled CA or web app to self-report their emotional wellbeing. Analysis of users’ experiences and engagement with the system for daily self-report suggests that despite many technical limitations, users rated self-reporting via CA as a significantly novel and attractive experience yielding similar rates of engagement to a traditional web-based method.

ColoriSens: An open-source and low-cost portable color sensor board for microfluidic integration with wireless communication and fluorescence detection

Microfluidic colorimetric biosensors have shown promising potential for detecting metal cations, anions, organic dyes, drugs, pesticides. As for today, most colorimetric sensors are read by a smartphone or professional optical imaging system, and there is still a lack of an affordable and reliable colorimetric detector for the microfluidic chip. Integrating those reading and detection capabilities into a microfluidic system is essential for point-of-care (POC) detection and can enable more complex microfluidic operations, such as lab-on-a-chip experiments or programmable microfluidics. We developed an open-source colorimetric detection sensor board that can be integrated into the existing microfluidic system. This sensor board has a built-in UV source that enables fluorescence detection. With built-in USB and Wi-Fi connectivity and a set of simple APIs, microfluidic systems can communicate directly with this sensor board, even wirelessly. The sensor was designed for low-cost. With a total build cost of less than 12 EUR per unit, it is ideal for low-cost systems and DIY microfluidic users. Along with the sensor board, we also designed a companion microfluidic chip carrier cartridge which can be modified depending on the chip’s dimension. To demonstrate the sensor, we also developed a cross-platform open-source client application to demonstrate the communication APIs and the functionality of the sensor board.

Indoor Localization Using Mind Evolutionary Algorithm-Based Geomagnetic Positioning and Smartphone IMU Sensors

With the pervasiveness and ubiquitous distribution of the magnetic field in indoor environments, indoor localization using magnetic positioning (MP) has attracted considerable attention. This work concentrates on the MP and pedestrian dead reckoning (PDR) method, and constructs a fusion system for smartphones using MP and PDR based on the extended Kalman filter (EKF). The mind evolutionary algorithm (MEA) is introduced to search for the optimal magnetic position based on a heuristic searching strategy, which uses the similartaxis and dissimilation for the evolutionary operation. In the PDR module, the acceleration characteristics of different walking patterns are analyzed and the corresponding features are extracted. The enhanced genetic algorithm-based extreme learning machine (EGA-ELM) is adopted to train these features and address the gait recognition problem of different walking patterns. Finally, to obtain a lightweight and high-precision fusion method, MEA-based MP is integrated with PDR based on the EKF. Extensive experiments are conducted to evaluate the proposed methods. The testing results showed that MEA-based MP can obtain a location error within 2.3 m and steps can be recognized with a mean accuracy of 95&#x0025; when different users participate in testing. The positioning results after fusion with PDR reveal that the mean location error and root-mean-square error (RMSE) are 1.25 m and 1.53 m respectively, which outperforms the MP, PDR, MP and PDR fusion methods using improved particle filter (IPF) and genetic particle filter (GPF).

The Monitoring of Oxygen Saturation and Heart Rate Using a Telemedicine Smartphone System (TMSS) as a New Innovation in the Health Sector

As a country with the world's sixth-largest population with 11.22% poor population, Indonesia encounters health problems as one of the main issues. According to the Progress Report in Asia &amp; the Pacific, Indonesia is still experiencing delays in the process of achieving the Millennium Development Goals. This study aimed to produce technological innovations related to the monitoring of oxygen saturation and heartbeat using the Telemedicine Smartphone System (TMSS). The contribution of this research is the resulting technological innovations related to the field of telemedicine that can be applied by Indonesian people at affordable prices. The monitoring tools will be effective and efficient when the tools are made with real-time monitoring of SpO2 and BPM using the TMSS system to improve Indonesia's development of health services. This design uses a fingertip sensor to detect oxygen saturation in the blood and an ear clip sensor to detect heartbeats, both sensors have an analog output which is conditioned in the Analog Signal Conditioning (ASC) circuit. The ASC’s output is processed on AT mega 328P. The resulting data is further displayed on the Liquid Character Display (LCD). Data are also sent via HC-05 Bluetooth and displayed on an Android Smartphone with SMS notifications. Based on the measurement results, the greatest error value was 0.220%, while the smallest was 0.055% on the SpO2. BPM had the largest error value of 2.33392%, while the smallest was 0.2283%. This research can be implemented into the transmission system using TMSS to improve the development of health services in Indonesia.

New approach to learning medical procedures using a smartphone and the Moodle platform to facilitate assessments and written feedback

Background: To overcome communication obstacles between medical students and trainers, we designed serial learning activities utilizing a smartphone and web-based instruction (WBI) on the Moodle platform to provide clear and retrievable trainer feedback to students on an objective structured clinical examination (OSCE) item. Methods: We evaluated students’ learning achievement and satisfaction with the new learning tool. A total of 80 fourth-year medical students participated. They installed the Moodle app (the WBI platform) on their smartphones and practiced an endotracheal suction procedure on a medical simulation mannequin while being evaluated by a trainer regarding competence in clinical skills on the smartphone app. Students’ competency was evaluated by comparing the scores between the formative assessment and the summative assessment. The degree of satisfaction and usefulness for the smartphone and WBI system were analyzed. Results: The means (standard deviations, SDs) of the formative and summative assessments were 8.80 (2.53) and 14.24 (1.97) out of a total of 17 points, respectively, reflecting a statistically significant difference (P&lt;0.05). The degree of satisfaction and perceived usefulness of the smartphone app and WBI system were excellent, with means (SDs) of 4.60 (0.58), and 4.60 (0.65), respectively. Conclusion: We believe that the learning process using a smartphone and the Moodle platform offers good guidance for OSCE skill development because trainers’ written feedback is recorded online and is retrievable at all times, enabling students to build and maintain competency through frequent feedback review.

Internet Financial Data Security and Economic Risk Prevention for Android Application Privacy Leakage Detection.

The rapid development of the Internet has brought great convenience to our lives, but it has also brought many problems. Due to the virtual nature of the Internet, many criminals conduct illegal and criminal activities in the virtual world. In the Internet, ordinary users account for the vast majority of Internet users, but at the same time, the information of ordinary users is also the easiest to steal, and malicious behaviors of stealing information of ordinary users continue to occur. Android system and iOS system are the two most common systems in the current smart phone system market. In the face of the current Internet chaos, both systems have exposed problems to varying degrees, especially the Android system. In order to protect the privacy of users, researchers have also begun to focus on the privacy protection of the Android system. Today, with the rapid development of mobile payments, the privacy of mobile phones is closely integrated with the security of users' property, and the resolution of privacy issues cannot be delayed. Now that the development of the financial industry has developed into the Internet, the Internet has provided a new place for financial development, but it also faces many risks. This requires Internet finance practitioners to formulate corresponding security protection systems based on the characteristics of the Internet. Starting from big data and based on the characteristics of Internet finance, this paper designs a data-centric Internet financial risk early warning system. The existence of this system can analyze the possible risks of Internet finance from the perspective of big data, enabling enterprises to prepare in advance, and effectively reducing the losses in the development of Internet finance.

Usability Evaluation of The Android Operating System Using Use Questionnaire

Usability aspects are used as a reference in measuring the level of usability of the Android operating system. Usability is important to use as an instrument to ensure an application can be useful according to user needs. This study aims to evaluate the usability of the Android operating system for users at universities. This study uses a use questionnaire that has aspects of effectiveness, efficiency, and satisfaction. Researchers used questionnaires with respondents at universities using the Android operating system for smartphone devices with 13 attributes of the questions asked. After obtaining the data, further analysis was carried out using the Likert scale method to obtain the percentage of usability values ​​on the Android operating system at the University. Usability measurement results show that all attributes have usability by the user, the average value is above 3, so it can be said that the android operating system used by the user already has a good usability aspect value. This study concludes that the android operating system with users at universities has a good adoption rate caused by easier use, ease to learn, and a good level of user satisfaction.

Micro-mechanical blood clot testing using smartphones

Frequent prothrombin time (PT) and international normalized ratio (INR) testing is critical for millions of people on lifelong anticoagulation with warfarin. Currently, testing is performed in hospital laboratories or with expensive point-of-care devices limiting the ability to test frequently and affordably. We report a proof-of-concept PT/INR testing system that uses the vibration motor and camera on smartphones to track micro-mechanical movements of a copper particle. The smartphone system computed the PT/INR with inter-class correlation coefficients of 0.963 and 0.966, compared to a clinical-grade coagulation analyzer for 140 plasma samples and demonstrated similar results for 80 whole blood samples using a single drop of blood (10 μl). When tested with 79 blood samples with coagulopathic conditions, the smartphone system demonstrated a correlation of 0.974 for both PT/INR. Given the ubiquity of smartphones in the global setting, this proof-of-concept technology may provide affordable and effective PT and INR testing in low-resource environments.

Storage Type and Hot Partition Aware Page Reclamation for NVM Swap in Smartphones

With the rapid advances in mobile app technologies, new activities using smartphones emerge every day including social network and location-based services. However, smartphones experience problems in handling high priority tasks, and often close apps without the user’s agreement when there is no available memory space. To cope with this situation, supporting swap with fast NVM storage has been suggested. Although swap in smartphones incurs serious slowing-down problems in I/O operations during saving and restoring the context of apps, NVM has been shown to resolve this problem due to its fast I/O features. Unlike previous studies that only focused on the management of NVM swap itself, this article discusses how the memory management system of smartphones can be further improved with NVM swap. Specifically, we design a new page reclamation algorithm for smartphone memory systems, which considers the following: (1) storage types of each partition (i.e., file system for flash storage and swap for NVM), and (2) access hotness of each partition including operation types and workload characteristics. By considering asymmetric I/O cost and access density for each partition, our algorithm improves the I/O performance of smartphones significantly. Specifically, it improves the I/O time by 15.0% on average and by up to 35.1% compared to the well-known CLOCK algorithm.

Mobile Contactless Fingerprint Recognition: Implementation, Performance and Usability Aspects.

This work presents an automated contactless fingerprint recognition system for smartphones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. In addition, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1360 contactless and contact-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our contactless scheme and the contact-based baseline scheme under constrained environmental influences. A comparative usability study on both capturing device types indicates that the majority of subjects prefer the contactless capturing method. Based on our experimental results, we analyze the impact of the current COVID-19 pandemic on fingerprint recognition systems. Finally, implementation aspects of contactless fingerprint recognition are summarized.

A Method for Automatic Android Malware Detection Based on Static Analysis and Deep Learning

The computers nowadays are being replaced by the smartphones for the most of the internet users around the world, and Android is getting the most of the smartphone systems’ market. This rise of the usage of smartphones generally, and the Android system specifically, leads to a strong need to effectively secure Android, as the malware developers are targeting it with sophisticated and obfuscated malware applications. Consequently, a lot of studies were performed to propose a robust method to detect and classify android malicious software (malware). Some of them were effective, some were not; with accuracy below 90%, and some of them are being outdated; using datasets that became old containing applications for old versions of Android that are rarely used today. In this paper, a new method is proposed by using static analysis and gathering as most useful features of android applications as possible, along with two new proposed features, and then passing them to a functional API deep learning model we made. This method was implemented on a new and classified android application dataset, using 14079 malware and benign samples in total, with malware samples classified into four malware classes. Two major experiments with this dataset were implemented, one for malware detection with the dataset samples categorized into two classes as just malware and benign, the second one was made for malware detection and classification, using all the five classes of the dataset. As a result, our model overcomes the related works when using just two classes with F1-score of 99.5%. Also, high malware detection and classification performance was obtained by using the five classes, with F1-score of 97%.

Multimodal Biometric Authentication for Smartphones

Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems