CityLightSense: A Participatory Sensing-based System for Monitoring and Mapping of Illumination levels

Adequate nighttime lighting of city streets is necessary for safe vehicle and pedestrian movement, deterrent of crime, improvement of the citizens’ perceptions of safety, and so on. However, monitoring and mapping of illumination levels in city streets during the nighttime is a tedious activity that is usually based on manual inspection reports. The advancement in smartphone technology comes up with a better way to monitor city illumination using a rich set of smartphone-equipped inexpensive but powerful sensors (e.g., light sensor, GPS, etc). In this context, the main objective of this work is to use the power of smartphone sensors and IoT-cloud-based framework to collect, store, and analyze nighttime illumination data from citizens to generate high granular city illumination map. The development of high granular illumination map is an effective way of visualizing and assessing the illumination of city streets during nighttime. In this article, an illumination mapping algorithm called Street Illumination Mapping is proposed that works on participatory sensing-based illumination data collected using smartphones as IoT devices to generate city illumination map. The proposed method is evaluated on a real-world illumination dataset collected by participants in two different urban areas of city Kolkata. The results are also compared with the baseline mapping techniques, namely, Spatial k-Nearest Neighbors, Inverse Distance Weighting, Random Forest Regressor, Support Vector Regressor, and Artificial Neural Network.

Access Control and Security System Via Bluetooth Application on Android Smartphone

Smartphone technology can be applied not only to establish communication needs but also to support other purposes. One of them is related to personal safety and security functions. It is undeniable that criminal acts can occur anytime and anywhere. Even in a private or residential area, theft could happen. Smartphone and sensor technology can be used as a solution to encounter this problem. In this case, it can be utilized to improve the security control system of the gate or garage door at home. This research presents a prototype of a gate and garage door control and security system that operates through an application on an android smartphone. The application of HC-05 Bluetooth is used to send signals from the smartphone to the Arduino Uno microcontroller, while the micro servo acts as a locking mechanism on the gate itself. The buzzer function is presented to notify homeowners when the gate or garage door is open for more than 15 seconds. This prototype can control gates and garage doors with an average connection time of only about 5 seconds. Thus, this prototype is feasible to use as an alternative to control and improve housing security systems.

Effect of Luminance and Contrast Variation on Stereoacuity Measurements Using Smartphone Technology

Owing to the limitations of printed stereoacuity tests, the effects of luminance and contrast on stereopsis have not yet been sufficiently investigated, despite its important implications in designing stereoacuity measuring instruments, particularly for electronic devices. A stereopsis measurement system was established using two 4 K smartphones and a phoropter to evaluate the effects of luminance and contrast variations on the stereoacuity test. Seventeen young subjects with normal visual acuity and stereopsis were recruited. Two types of test symbols, contour-based and random-dot-based, were used in the experiment. Four series tests were established with different maximum brightness values, including 240 lux, 120 lux, 60 lux, and 30 lux. Each series test contained 19 pages with different contrasts between 95% and 5% and was calculated using the Michelson contrast formula. No significant difference was found for both contour-based and random-dot-based stereograms in any of the contrast groups with different maximum brightness. Similarly, no significant difference was found between contour-based and random-dot-based patterns under different contrasts of above 35%. As the contrast decreased below 30%, the stereopsis was significantly better in the contour-based pattern than in the random-dot-based pattern for some degrees of contrast. The luminance and contrast of the digital display are not critical factors for stereoacuity under normal circumstances. This implies that a standard monitor with a certain 3D technology can be used to measure the stereoacuity threshold without calibrating the luminance and contrast.

Evaluating Smartphone App Based Dietary Tracking in a Military Population: Validation Study (Preprint)

BACKGROUND Collecting dietary intake data is a key component for a majority of nutritional epidemiology studies. Smartphone technology advancements allow researchers to use health and nutrition apps as alternatives to currently available tools (food frequency questionnaires, 24-hour recalls, and food diaries). Service Members (SM) can greatly benefit from the always-available information and easily accessible nature of smartphones to track their intake. Clinicians working with military units can help provide these SM with the skills to evaluate their intake for performance benefits. Understanding the accuracy of these apps is important to determine their effectiveness for use in clinical and research settings. OBJECTIVE This study evaluated the relative validity of self-reported intake with the HealthWatch 360 (HW 360) app compared to the Automated Self-Administered 24-hour Dietary Assessment (ASA24). METHODS Recruitment targeted Army and Air Force SM from Joint Base Lewis-McChord, WA and Joint Base San Antonio-Lackland, TX who currently or previously failed to meet body composition standards. Participants (n=53) completed a demographic questionnaire, baseline anthropometric measurements, and recorded daily intake on the HW 360 app. They returned approximately two weeks later to complete a 24-hour recall using the ASA24. Agreement and relative validity were evaluated using Bland-Altman plots and two one-sided tests at a ± 10% equivalency range of ASA24 mean nutrient intake values between HW 360 and ASA24 data. Multilinear regressions analyzed relationships between participant demographics and relative validity. RESULTS HW 360 was not significantly equivalent to the ASA24. Large levels of underreporting were found in total energy (Mean Difference (Mdiff) = -503.3 kcal, 90% CI: -649.8 to -356.7 kcal), carbohydrates (Mdiff = -52.2 g, 90% CI: -70.4 to -34.1 g), protein (Mdiff = -20.4 g, 90% CI: -29.4 to -11.3 g), and fat (Mdiff = -24.6 g, 90% CI: -32.5 to -16.7 g). Bland-Altman plots failed to illustrate agreement. No significant correlations existed for demographic variables and relative validity. CONCLUSIONS Differences between all variables tested were above clinically significant values and limit the usage of this application in research and clinical settings. Further research is needed to determine the potential causes of underreporting and evaluate methods to minimize this effect. Understanding these effects allows the implementation of a tailored app for use with SM. It has the potential to be an invaluable asset for this population due the unpredictable nature of deployments and training exercises. CLINICALTRIAL ClinicalTrials.gov NCT04959318; https://clinicaltrials.gov/ct2/show/NCT04959318

Integration of smartphone technology for maize recognition

The study of android based maize assessment was done by involving two popular machine learning software i.e. teachable machine and android studio. The classification model was performed in online teachable machine learning while interface generation was performed in android studio. Various maize tassel from male, female and contamination plants were collected and used for training and model validation. The results indicated that Android-based tassel classification was successfully applied to the study area with accuracy of 80.7%. In addition, the error of classification was 19.3%, a relatively lower values for large testing datasets. Several mis-classification were found particularly at similar tassel shape. The integration of the model with smartphone technology enables rapid recognition of off-type plant at real-time, even though operated by personnel with limited skills or no knowledge seed technology on maize parental lines ideotype.

Mata: An Android Eye-Tracking Based User Interface Control Application

The advent of smartphone technology has provided us with intelligent devices for communication as well as playing game. Unfortunately, applications that exploit available sensors in the smartphone are mostly designed for people with no physical handicap. This paper presents Mata, a game user interface using eye-tracking to operate and control games running on Android smartphone. This system is designed to enhance user experiences and help motoric impaired peoples in using smartphone for playing games. Development and testing of the Mata system has proven the concepts of eye-tracking and eyegazing usage as unimodal input for game user interface.