Android Smartphone App Research Articles

An Objective Assessment of Neuromotor Control Using a Smartphone App After Repeated Subconcussive Blast Exposure.

Subconcussive blast exposure has been shown to alter neurological functioning. However, the extent to which neurological dysfunction persists after blast exposure is unknown. This longitudinal study examined the potential short- and long-term effects of repeated subconcussive blast exposure on neuromotor performance from heavy weapons training in military personnel. A total of 214 participants were assessed; 137 were exposed to repeated subconcussive blasts and 77 were not exposed to blasts (controls). Participants completed a short stepping-in-place task while an Android smartphone app placed on their thigh recorded movement kinematics. We showed acute suppression of neuromotor variability 6 h after subconcussive blast exposure, followed by a rebound to levels not different from baseline at the 72 h, 2-week, and 3-month post-tests. It is postulated that this suppression of neuromotor variability results from a reduction in the functional degrees of freedom from the subconcussive neurological insult. It is important to note that this change in behavior is short-lived, with a return to pre-blast exposure movement kinematics within 72 h.

An enhanced image based mobile deep learning model for identification of Newcastle poultry disease

An enhanced mobile deep learning model based on images is presented in this paper to identify Newcastle poultry disease. A dataset of manually annotated and labeled images of the disease was utilized to pre-train an image-based Convolutional Neural Network (CNN). An Android smartphone app was developed to communicate with the model. A local server was integrated with the generated model to do image classification. A mobile application was developed and made available, enabling users to upload a fecal photograph to a website housed on the streamlet server and obtain the model's processed findings. The user regains control over their health status. The model achieved an accuracy of 95% on the test set and was able to correctly identify specific instances of Newcastle poultry disease. The paper discusses the advantages of a mobile-based approach in comparison to traditional methods of identification and proposes the model as an effective low-cost solution for farmers and researchers.

Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication

Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception sensors like cameras to detect risk and issue collision warnings or apply emergency braking. Perception sensors like cameras are highly affected by lighting and weather conditions. Cameras, radar, and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs on trajectories with a potential collision risk. An Android smartphone app with low-energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data are smoothed using a Kalman filter, and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU, and issues driver warnings with different severity levels. The warning severity level is based on the analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision-mitigation system.

Android accident detection and alert system

Android smartphone app will automatically detect the accident that will be occurred. The app immediately retrieves the GPS location and activates an alarm screen. With one tap, the user can request emergency assistance. The app calls the local emergency number and sends SMS alerts to predefined contacts. The alerts provide accident details including location coordinates, number of passengers, and crash characteristics. Development challenges include accurately identifying crashes across varying conditions while minimizing false positives, integrating with onboard vehicle systems for additional data, addressing cybersecurity risks, optimizing for minimal battery usage, and easing privacy concerns over data collection. If technical obstacles can be overcome, such an automated crash detection and emergency alert app could help expedite emergency responses and improve road safety outcomes. Intended as an low-cost, scalable supplementary crash alert system, it has the potential to save lives worldwide.

Development of a Mobile Health Application Based on a Mixed Prenatal Care in the Context of COVID-19 Pandemic.

We describe the development and structure of a novel mobile application in a mixed model of prenatal care, in the context of the COVID-19 pandemic. Furthermore, we assess the acceptability of this mobile app in a cohort of patients. First, we introduced a mixed model of prenatal care; second, we developed a comprehensive, computer-based clinical record to support our system. Lastly, we built a novel mobile app as a tool for prenatal care. We used Flutter Software version 2.2 to build the app for Android and iOS smartphones. A cross-sectional study was carried out to assess the acceptability of the app. A mobile app was also built with the main attribute of being connected in real-time with the computer-based clinical records. The app screens detail information about activities programmed and developed in the prenatal care according to gestational age. A downloadable maternity book is available and some screens show warning signs and symptoms of pregnancy. The acceptability assessment was mostly rated positively regarding the characteristics of the mobile app, by 50 patients. This novel mobile app was developed as a tool among pregnant patients to increase the information available about their pregnancies in the provision of a mixed model of prenatal care in the context of the COVID-19 pandemic. It was fully customized to the needs of our users following the local protocols. The introduction of this novel mobile app was highly accepted by the patients.

Reliability and Confirmatory Factor Analysis (CFA) of a Paper- Versus App-Administered Resilience Scale in Scottish Youths: Comparative Study.

BackgroundAdequately measuring resilience is important to support young people and children who may need to access resources through social work or educational settings. A widely accepted measure of youth resilience has been developed previously and has been shown to be suitable for vulnerable youth. While the measure is completed by the young person on paper, it has been designed to be worked through with a teacher or social worker in case further clarification is required. However, this method is time consuming and, when faced with large groups of pupils who need assessment, can be overwhelming for schools and practitioners. This study assesses app software with a built-in avatar that can guide young persons through the assessment and its interpretation.ObjectiveOur primary objective is to compare the reliability and psychometric properties of a mobile software app to a paper version of the Child and Youth Resilience measure (CYRM-28). Second, this study assesses the use of the CYRM-28 in a Scottish youth population (aged 11-18 years).MethodsFollowing focus groups and discussion with teachers, social workers, and young people, an avatar was developed by a software company and integrated into an android smartphone app designed to ask questions via the device’s inbuilt text-to-voice engine. In total, 714 students from 2 schools in North East Scotland completed either a paper version or app version of the CYRM-28. A cross-sectional design was used, and students completed their allocated version twice, with a 2-week period in between each testing. All participants could request clarification either from a guidance teacher (paper version) or from the in-built software glossary (app version).ResultsTest and retest correlations showed that the app version performed better than the paper version of the questionnaire (paper version: r303=0.81; P<.001; 95% CI 0.77-0.85; app version: r413=0.84; P<.001; 95% CI 0.79-0.89). Fisher r to z transformation revealed a significant difference in the correlations (Z=–2.97, P<.01). Similarly, Cronbach α in both conditions was very high (app version: α=.92; paper version: α=.87), suggesting item redundancy. Ordinarily, this would lead to a possible removal of highly correlated items; however, our primary objective was to compare app delivery methods over a pen-and-paper mode and was hence beyond the scope of the study. Fisher r to z transformation revealed a significant difference in the correlations (Z=–3.69, P<.01). A confirmatory factor analysis supported the 3-factor solution (individual, relational, and contextual) and reported a good model fit (χ215=27.6 [n=541], P=.24).ConclusionsALEX, an avatar with an integrated voice guide, had higher reliability when measuring resilience than a paper version with teacher assistance. The CFA reports similar structure using the avatar when compared against the original validation.

Practitioners' and Policymakers' Successes, Challenges, Innovations, and Learning in Promoting Children's Well-being During COVID-19: Protocol for a Multinational Smartphone App Survey.

BackgroundThe advent of COVID-19 abruptly thrust the health and safety of children and families into greater risk around the world. As regional and local governments, nongovernmental organizations, communities, families, and children grapple with the immediate public health impact of COVID-19, the rights and well-being of children, especially those who are already marginalized, have been overlooked. Those working with children have likely encountered unprecedented challenges and responded in innovative ways in efforts to address the needs and rights of all children.ObjectiveThis paper presents a protocol for a large-scale, multinational study using a new smartphone app to capture the real-time experiences and perspectives of practitioners and policymakers supporting children and families during the COVID-19 pandemic around the globe in relation to a children’s human rights 4P framework of protection, provision, prevention, and participation.MethodsThis protocol describes a mixed methods survey utilizing a custom-built iOS and Android smartphone app called the COVID 4P Log for Children’s Wellbeing, which was developed in close consultation with 17 international key partner organizations. Practitioners and policymakers working with and for children’s well-being across 29 countries and 5 continents were invited to download the app and respond to questions over the course of 8 weeks. The anticipated large amount of qualitative and quantitative response data will be analyzed using content analysis, descriptive statistics, and word frequencies.ResultsFormal data collection took place from October 2020 until March 2021. Data analysis was completed in July 2021.ConclusionsThe findings will directly inform the understanding of the ways in which COVID-19 has impacted practitioners’, managers’, and policymakers’ efforts to support children’s well-being in their practices, services, and policies, respectively. Innovative and ambitious in its scope and use of smartphone technology, this project also aims to inform and inspire future multinational research using app-based methodologies—the demand for which is likely to continue to dramatically rise in the COVID-19 era. Mitigating the risks of longitudinal remote data collection will help maximize the acceptability of the app, respondents’ sustained engagement, and data quality.International Registered Report Identifier (IRRID)DERR1-10.2196/31013

Controlling and Monitoring a Robot-Car Based on Smart Phone Applications

A robot-car is typically an electro-mechanical system driven by electronic programming and computer programming. It can be managed by an Android smartphone APP. The remote buttons in the android app have been created in this paper by monitoring the motion of the car with them, using Bluetooth communication for the controller and android interfaces. The controller can be attached via UART to the Bluetooth module. The robot motion can be controlled according to commands obtained from Android. A robot in a car controlled by Android smartphones using Bluetooth waves will be presented. A video camera will send a video signal to the iPhone or Android device via Wi-Fi without the internet. The implementation will be shown and discussed clearly. The purposes of using this aspect are many and different such as tracking theft cars or exploring areas where humans cannot reach because of danger, pollution, or infested places. The consistent output of a robotic system along with quality and repeatability are unmatched.

A mobile app for tracking non-motor symptoms of people with Parkinson’s disease: A usability study

Smart phone-based technology for people with Parkinson’s disease has been developed worldwide. Unmonitored non-motor symptoms decrease quality of life of people with Parkinson’s disease, so the needs for technology to manage non-motor symptoms are increasing. The technology is needed to detect subtle changes in non-motor symptoms by healthcare professional. There is no mobile app which manage comprehensive symptoms of Parkinson’s disease including non-motor symptoms. It is necessary to develop a new tracking system that can effectively manage non-motor symptoms as well as motor symptoms of Parkinson’s disease. We developed a prototype of mobile app for Android smartphones, with cooperation with Mazelone company. we also have shaped functions for monitoring of motor symptoms and medication adherence. It also provided a section for caregivers to use on behalf of people with Parkinson’s disease who have difficulty to use app due to hand tremor. Through Delphi technique, we obtained content validity from eight medical and nursing experts on the contents of the application. We provided regular telephone counseling to improve and encourage their app usage. Fifteen participants used the app for 6 weeks. To evaluate usability of mobile app, we provided constructed questionnaire and conducted individual telephone interview. A mobile app for tracking non-motor symptoms demonstrated high usability and satisfaction. We learned lessons about facilitators and barriers when implementing an app such as perception and acceptance of mobile technology. The mobile app will improve continuum of care. Future studies need to improve the contents and refine technical approach for people with Parkinson’s disease.

HU‐PageScan: a fully convolutional neural network for document page crop

November The offer of online, automated, and impersonal services demand users to upload scanned copies of their documents to the organisations. As a consequence of this decentralisation, the documents present more challenges to the already complex process of image processing and information extraction. To address this problem, the authors presented an optimised fully convolutional neural network model for document segmentation that works on mobile devices to detect the region of the document in the captured image. They performed experiments in three representative datasets comparing the proposed method with the Geodesic object Proposals, U-net, Mask R-CNN, and OctHU-PageScan algorithms. They also compared the proposed model with all competitors of the ICDAR2015 Competition on smartphone document capture. Furthermore, they performed a qualitative and comparative analysis with the CamScanner software, a popular app for Android and iOS smartphones used for more than 100 million users in over 200 countries. The proposed approach achieved a significant performance compared with the current state-of-the-art methods, providing a powerful approach for document segmentation in photos and scanned images.

Protocol of the STRess at Work (STRAW) Project: How to Disentangle Day-to-Day Occupational Stress among Academics Based on EMA, Physiological Data, and Smartphone Sensor and Usage Data.

Several studies have reported on increasing psychosocial stress in academia due to work environment risk factors like job insecurity, work-family conflict, research grant applications, and high workload. The STRAW project adds novel aspects to occupational stress research among academic staff by measuring day-to-day stress in their real-world work environments over 15 working days. Work environment risk factors, stress outcomes, health-related behaviors, and work activities were measured repeatedly via an ecological momentary assessment (EMA), specially developed for this project. These results were combined with continuously tracked physiological stress responses using wearable devices and smartphone sensor and usage data. These data provide information on workplace context using our self-developed Android smartphone app. The data were analyzed using two approaches: 1) multilevel statistical modelling for repeated data to analyze relations between work environment risk factors and stress outcomes on a within- and between-person level, based on EMA results and a baseline screening, and 2) machine-learning focusing on building prediction models to develop and evaluate acute stress detection models, based on physiological data and smartphone sensor and usage data. Linking these data collection and analysis approaches enabled us to disentangle and model sources, outcomes, and contexts of occupational stress in academia.

Finding Comfortable Routes for Ambulance Transfers of Newborn Infants.

Early inter-hospital ambulance transport of premature babies is associated with more severe brain injury. The mechanism is unclear, but they are exposed to excessive noise and vibration. Smart-routing may help minimise these exposure levels and potentially improve outcomes.An app for Android smartphones was developed to collect vibration, noise and location data during ambulance journeys. Four smartphones, with the app installed, were provided to the local neonatal transport group to attach to their incubator trolleys. An example of route comparison was performed on the roads used between Nottingham City Hospital (NCH) and Leicester Royal Infirmary (LRI).Almost 1,700 journeys were recorded over the space of a year. 39 of these journeys travelled from NCH to LRI, comprising of 9 different routes. Analysis was performed on all recorded data which travelled along each road. For routes from NCH to LRI, the route with least vibration was also the quickest. Noise levels, however, were found to increase with vehicle speed. Ambulance drivers in the study did not tend to take the quickest, smoothest or quietest route.Android smartphones are a practical method of gathering information about the in-ambulance environment. Routes were found to vary in vibration, noise and speed, suggesting these could be minimised. The next step is to combine recorded and clinical data to try and define an ideal neonatal comfort metric which can then be fed into the routing. Roll-out of the app around the UK is also planned.Clinical relevance-Transferring preterm neonatal infants to specialist units lead to worse outcomes. By reducing the levels of vibration and noise the infants are exposed to during transport, we hope to improve outcomes.

AMEn Predictor: A mobile app to predict energy values of broilers feedstuffs

This application note introduces an app for Android smartphones to predict the nitrogen-corrected metabolizable energy (AMEn) values of feedstuffs for broilers by using an artificial neural network model. This application, called AMEn Predictor, has been developed for Android devices. To calculate the AMEn prediction, the user has to insert values of the chemical composition of one concentrate feedstuff sample and the dry matter value to consider in the prediction. The dataset contains 48 bioassay results used to test the validation and efficiency of the tool. All predictions were processed instantly, with no internet connection required. Findings indicated a low value of mean absolute percentage error (MAPE = 5.92%) in the test dataset. The results confirm the fact that AMEn Predictor enables the animal nutritionists to obtain quick and accurate predictions of the AMEn for feedstuff samples, generally used by the poultry feed industry. AMEn Predictor was the first app for the prediction of broilers’ feedstuffs energy values currently freely available on Google Play.

A Blended Learning System to Improve Motivation, Mood State, and Satisfaction in Undergraduate Students: Randomized Controlled Trial.

BackgroundSmartphone-based learning, or mobile learning (m-learning), has become a popular learning-and-teaching strategy in educational environments. Blended learning combines strategies such as m-learning with conventional learning to offer continuous training, anytime and anywhere, via innovative learning activities.ObjectiveThe main aim of this work was to examine the short-term (ie, 2-week) effects of a blended learning method using traditional materials plus a mobile app—the iPOT mobile learning app—on knowledge, motivation, mood state, and satisfaction among undergraduate students enrolled in a health science first-degree program.MethodsThe study was designed as a two-armed, prospective, single-blind, randomized controlled trial. Subjects who met the inclusion criteria were randomly assigned to either the intervention group (ie, blended learning involving traditional lectures plus m-learning via the use of the iPOT app) or the control group (ie, traditional on-site learning). For both groups, the educational program involved 13 lessons on basic health science. The iPOT app is a hybrid, multiplatform (ie, iOS and Android) smartphone app with an interactive teacher-student interface. Outcomes were measured via multiple-choice questions (ie, knowledge), the Instructional Materials Motivation Survey (ie, motivation), the Profile of Mood States scale (ie, mood state), and Likert-type questionnaires (ie, satisfaction and linguistic competence).ResultsA total of 99 students were enrolled, with 49 (49%) in the intervention group and 50 (51%) in the control group. No difference was seen between the two groups in terms of theoretical knowledge gain (P=.92). However, the intervention group subjects returned significantly higher scores than the control group subjects for all postintervention assessed items via the motivation questionnaire (all P<.001). Analysis of covariance (ANCOVA) revealed a significant difference in the confusion and bewilderment component in favor of the intervention group (P=.01), but only a trend toward significance in anger and hostility as well as total score. The intervention group subjects were more satisfied than the members of the control group with respect to five out of the six items evaluated: general satisfaction (P<.001), clarity of the instructions (P<.01), clarity with the use of the learning method (P<.001), enough time to complete the proposed exercises (P<.01), and improvement in the capacity to learn content (P<.001). Finally, the intervention group subjects who were frequent users of the app showed stronger motivation, as well as increased perception of greater gains in their English-language competence, than did infrequent users.ConclusionsThe blended learning method led to significant improvements in motivation, mood state, and satisfaction compared to traditional teaching, and elicited statements of subjective improvement in terms of competence in English.Trial RegistrationClinicalTrials.gov NCT03335397; https://clinicaltrials.gov/ct2/show/NCT03335397

A novel surgical technique for spontaneous intracerebral hematoma evacuation.

Stereotactic removal of intracerebral hematoma is a routine procedure for treating hypertensive intracerebral hemorrhage, but the complex sequence of operations limits its adoption. We explored the application of a novel surgical technique for the removal of spontaneous intracerebral hematomas. The surgical technique based on computed tomography (CT) images was used in hematoma projection and surgical planning. Markers placed on the scalp based on an Android smartphone app allowed the installation of a stereotactic head frame to facilitate the selection of the best trajectory to the hematoma center for removing the hematoma. Forty-two patients with spontaneous intracerebral hemorrhage were included in the study, including 33 cases of supratentorial hemorrhage, 5 cases of cerebellum hemorrhage, and 4 cases of brain stem hemorrhage. The surgical technique combined with the stereotactic head frame helped the tip of the drainage tube achieve the desired position. The median surgical time was 45 (range 25-75)min. The actual head frame operating time was 10 (range 5-15)min. Target alignment performed by the surgical technique was accurate to ≤ 10.0mm in all 42 cases. No patient experienced postoperative rebleeding. In 33 cases of supratentorial intracerebral hemorrhage, an average evacuation rate of 77.5% was achieved at postoperative 3.1 ± 1.4days, and 29 (87.9%) cases had a residual hematoma of < 15ml. The novel surgical technique helped to quickly and effortlessly localize hematomas and achieve satisfactory hematoma removal. Clinical application of the stereotactic head frame was feasible for intracerebral hemorrhage in various locations.

RECOVER-E \u2013 a mobile app for patients undergoing total knee or hip replacement: study protocol

BackgroundTotal knee replacement (TKR) or total hip replacement (THR) are common and effective procedures in patients with osteoarthritis (OA) to restore physical function and reduce joint related pain. Patient education plays an important role in the treatment process aiming to develop necessary self-management skills to facilitate recovery and ensure long-term success. We have developed a mobile app (RECOVER-E) for iOS and Android smartphones which provides important information on the preoperative phase, surgery and recovery. The concomitant study will determine the efficacy of RECOVER-E on patients’ surgical outcomes.Methods/designThis study is a non-randomized, multi-centre (4 sites), double-arm, controlled trial with 1:1 assignment. 160 patients undergoing primary TKR or THR will be recruited from January until October 2019 in 4 German hospitals. Both groups will receive standard care. Additionally, the intervention group will use the app RECOVER-E. Measurements will be taken 4–6 weeks before surgery, on the day of admission to the hospital, on the first and 7th postoperative day and 3 months post-surgery. Primary outcome will be self-reported physical function measured on the activities of daily living (ADL) subscale of the Knee injury and Osteoarthritis Outcome Score (KOOS) and the Hip disability and Osteoarthritis Outcome Score (HOOS) for patients with knee and hip osteoarthritis, respectively.Secondary outcomes include the subscales for pain, symptoms, function in sport and recreation and knee/hip-related quality of life of the HOOS and KOOS, preoperative anxiety, measured by the Hospital Anxiety and Depression Scale (HADS), as well as, pain at rest and pain during activity measured by a numerical rating scale (NRS). Primary endpoint is 3 months post-surgery.DiscussionMobile Health (mHealth) has become increasingly important in patient-centred health care aiming to enhance patient involvement and self-management capabilities. To our knowledge this is the first study to investigate the effect of an evidence-based mobile app on patient reported outcomes after joint replacement. This study should provide evidence supporting the use of mHealth to facilitate recovery and open up new possibilities for patient care in joint replacement.Trial registrationDRKS Data Management retrospectively registered. DRKS-ID: DRKS00012744.

Steps to Develop a Mobile App for Pain Assessment of Cancer Patients: A Usability Study.

Health-related mobile apps have the potential to allow patients and providers to proactively and responsibly manage pain together. However, there is a gap between the science of pain and current mobile apps. To develop a prototype science-based pain assessment mobile app (PainSmart) for Android smartphones, pain assessment tasks were extracted from a clinical guideline. These tasks were then converted to activity diagrams and became the logic of PainSmart. Five participants diagnosed with breast cancer evaluated usability of PainSmart with the System Usability Scale. Patient experience was recorded using Camtasia Studio Version 9 software. The five participants were able to explore the pain app after only 20 minutes of training. Using the System Usability Scale with comments, participant mean usability score was 77.5; above 68 is considered an above average system. A prototype of a pain assessment mobile app for cancer patients demonstrated high usability and will be refined based on participant feedback.

122 3OLLIN: APP to Assess the Risk of Falls in Older Adults

Abstract Objective To develop an app for Android smartphones (named 3Ollin) that assess the fall risk through the automated analysis of three physical performance (PP) tests and the history of falls of the participant. Design Cross-sectional design. Setting Community-dwelling older adults studied at the Functional Evaluation Laboratory at the National Institute of Geriatrics in Mexico. Participants Older adults (n=223) living in Mexico City were included. The participants were recruited from groups of pensioners from the National Autonomous University of Mexico, church groups, and other community programs. Measurements Data were collected about demographics, comorbidities, cognitive status, nutritional status, dependency and PP variables. The 3Ollin app evaluates the PP of the participants through the history of falls, the Timed Up and Go, the 30-second chair stand, and the 4-stage balance tests. The app incorporates them into the CDC’s Algorithm for Fall Risk Screening, Assessment, and Intervention. Methods All participants were evaluated by trained staff. The app has a registration system for users and patients, allows to perform the PP tests, acquires the data from the sensors signals, and sends and stores the information in the server of the National Institute of Geriatrics in Mexico. Results The mean age of the studied population was 73.5±7.0 years, 77.6% were women, and 37.2% did not complete the 4-stage balance test. The mean percentage error (MPE) between the gait speed measured through the GAITRite walkway (ms=103.5±26.1 cm/s) and the 3Ollin app (ms=100.0±30.3 cm/s) was 10.7%. The MPE between the number of chair lifts in 30 s according to the evaluator's observations (mCL=15±6 repetitions), and the 3Ollin app (mCL=19±6 repetitions) was 23.4%. Additionally, 87% were correctly classified according to the 3Ollin app. Conclusions This design first step of the 3Ollin app probed to offer a valid, efficient, costless performance evaluation, without the need of a laboratory environment.

Crowdsourcing of environmental noise map using calibrated smartphones

Smartphones have been used for crowdsourcing of environmental noise data and generation of noise maps. The main issue is the accuracy of smartphones for sound measurement due to huge diversity of smartphone models available in the market. This paper reports an experimental study with a group of twenty-nine graduate students to demonstrate the viability of crowdsourcing of environmental noise data using smartphones. Central to the viability of this study is an in-house developed app for both Android and iOS smartphones and the associated calibration method which makes the smartphones accurate enough for noise mapping. This study has demonstrated the viability of using calibrated smartphones for the monitoring of environmental noise. There is also a potential to use calibrated smartphones for generation of noise maps on a larger scale for cities around the world.

Autistic Innovative Assistant (AIA): an Android application for Arabic autism children

ABSTRACT Most autism centers in Arabic countries rely on manual techniques in teaching children the basics of language, math, and social skills. Actually, these techniques may be limited in measuring the child’s progress in a systematic way considering different fields. To overcome these limitations, we developed Autistic Innovative Assistant (AIA) which is an Android smartphone app that is dedicated to teach Arabic autistic children the necessary linguistic and mathematical basics in addition to improving their social skills through creating an interactive learning environment. The proposed app covers five main categories with an interactive quiz, included in each, as an assessment tool. The lessons included in each category are viewed as a series of colorful pictures with a written sentence and an audio message to describe the meaning of each, thereby attracting the child’s attention and making learning a fun activity. Each child’s supervisor can access his/her quizzes’ records, measure the child’s progress in different categories, and use the related recommendation reports to adjust the app usage pattern. AIA was tested by a group of autistic children for a one-month test period in a real life environment represented in “Jordan Specialized Center for Autism” and the results and feedback were very promising.