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842 results for “Smartphone”
Dataset of "Raising Awareness for Inertial Sensors-based Keylogging on Smartphones" scientific research
<p>Dataset for the article</p> <p>Federico Montori, Luca Sciullo, and Luca Bedogni. 2024. Raising Awareness for Inertial Sensors-based Keylogging on Smartphones. In Proceedings of the 2024 International Conference on Information Technology for Social Good (GoodIT '24). Association for Computing Machinery, New York, NY, USA, 14–21. https://doi.org/10.1145/3677525.3678634</p> <p>Please cite the above paper if you are using this dataset.</p>
FIGURE 3. A. Low immersion conditions used a handheld smartphone. B. High immersion conditions used a in Designing scientifically-grounded paleoart for augmented reality at La Brea Tar Pits
FIGURE 3. A. Low immersion conditions used a handheld smartphone. B. High immersion conditions used a smartphone inserted into an inexpensive headset. C. To provide binocular vision in the headset, the smartphone screen is split into two smaller images, greatly reducing available screen space and resolution.
Figure 4.The purpose for using Smartphones-Micro Learning: A Modernized Education System
<p>Figure 4 shows the purpose of using smartphones. The current status implies that a maximum number of 91% of the respondents use it for social networking. 77% of the respondents use it for communication (WhatsApp, Viber, etc.) followed by e-mails at 55%. Only 43% of the respondents use it for learning and updating (translation, dictionary, etc). Comparing figure 3 andfigure 4 the research identified the gap between electronic device which they prefer for learning and the usage of that electronic devices for learning and updating.</p>
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
Dataset used in "Free context smartphone based application for motor activity levels recognition"
<p>This is the data set used in the paper "Free context smartphone based application for motor activity levels recognition", 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, 2016, pp1-4.</p> <p>The data refer to three subjects (i.e. subject1, subject2 and subject3). For each subject a folder is created. The folder contains data used for training and for test in all the conditions addressed by the reference paper.</p> <p>Activities are labeled by the last character of the filename as follows: 1-2 resting; 3-6 walking; 7-8 running; 9-12 climbing stairs</p>
ICDAR2015 competition on smartphone document capture and OCR (SmartDoc) - Challenge 2
<p><strong>ICDAR2015 competition on smartphone document capture and OCR (SmartDoc)</strong></p> <p><strong>Challenge 2: MOBILE OCR COMPETITION</strong></p> <p>The goal of the competition is to extract the textual content from document images which are captured by mobile phones. The images are taken under varying conditions to provide a challenging input. The dataset was prepared for ICDAR2015-SmartDoc competition. For more details about the dataset please visit the competition's website:</p> <p>https://sites.google.com/site/icdar15smartdoc/home</p> <p>http://smartdoc.univ-lr.fr</p> <p>You may also refer to the following paper for more details on the ICDAR2015-SmartDoc competition:</p> <p>Jean-Christophe Burie, Joseph Chazalon, Mickaël Coustaty, Sébastien Eskenazi, Muhammad Muzzamil Luqman, Maroua Mehri, Nibal Nayef, Jean-Marc OGIER, Sophea Prum and Marçal Rusinol: “ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc)”, In 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.</p> <p><strong>If you use this dataset, please send us a short email at <icdar.smartdoc (at) gmail.com> to tell us why it was useful to you, and whether you have results or publications we can reference on our website. Thank you!</strong></p>
Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2016 Competition (Alcalá, Spain).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 17 training logfiles and a subfolder with the 9 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 578 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A.; Knauth, A.; Moreira, A.; Beer, Y.; Fetzer, T.; Ta, V.-C.; Montoliu, R.; Seco, F.; Mendoza, G.; Belmonte, O.; Koukofikis, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Ebner, F.; Deinzer, F.; Vaufreydaz, D.; Dao, T.-K.; and Castelli, E. The Smartphone-based Off-Line Indoor Location Competition at IPIN 2016: Analysis and Future work Sensors Vol. 17(3), 2017. <a href="http://dx.doi.org/10.3390/s17030557">http://dx.doi.org/10.3390/s17030557</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Montoliu, R.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2791530">http://dx.doi.org/10.5281/zenodo.2791530</a></li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2016/competition-home">http://evaal.aaloa.org/2016/competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2016track3/">http://indoorloc.uji.es/ipin2016track3/</a> </li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p> </p>
NIH-funded smartphone intervention apps: 2014-2018
<p>This dataset includes our categorical analysis of 399 abstracts retrieved from NIH Reporter that included some form of smartphone app and that included an intervention strategy. </p>
Benchmarking Smartphone Fluorescence-Based Microscopy with DNA Origami Nanobeads: Reducing the Gap toward Single-Molecule Sensitivity
<p>Smartphone-based fluorescence microscopy has been rapidly developing over the last few years, enabling point-of-need detection of cells, bacteria, viruses, and biomarkers. These mobile microscopy devices are cost-effective, field-portable, and easy to use, and benefit from economies of scale. Recent developments in smartphone camera technology have improved their performance, getting closer to that of lab microscopes. Here, we report the use of DNA origami nanobeads with predefined numbers of fluorophores to quantify the sensitivity of a smartphone-based fluorescence microscope in terms of the minimum number of detectable molecules per diffraction-limited spot. With the brightness of a single dye molecule as a reference, we compare the performance of color and monochrome sensors embedded in state-of-the-art smartphones. Our results show that the monochrome sensor of a smartphone can achieve better sensitivity, with a detection limit of ∼10 fluorophores per spot. The use of DNA origami nanobeads to quantify the minimum number of detectable molecules of a sensor is broadly applicable to evaluate the sensitivity of various optical instruments.</p>
14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: comparison with the PHQ-9 depression screening
<p>This dataset contains 14 days of ambulatory assessment (AA) depression symptoms and mood ratings with timestamps, a retrospective Patient Health Questionnaire (PHQ-9) assessment and the demographic variables age and gender.</p> <p>The AA was conducted with users of the mobile mental health / depression screening app "Moodpath". ICD-10 depression symptoms were assessed with the following questions:</p> <p>ICD-10 symptom 1: "depressed mood"<br> q1 = Are you feeling depressed?<br> q2 = Are you feeling hopeless?</p> <p>ICD-10 symptom 2: "loss of interest and enjoyment"<br> q3 = Do you feel like you are not interested in anything right now?<br> q4 = Do you have less pleasure in doing things you usually enjoy?</p> <p>ICD-10 symptom 3: "increased fatigability"<br> q5 = Do you currently have considerably less energy? <br> q6 = Are your everyday tasks making you very tired currently?</p> <p>ICD-10 symptom 4: "reduced concentration and attention"<br> q11 = Is it hard for you to make decisions currently? <br> q12 = Is it hard for you to concentrate currently?</p> <p>ICD-10 symptom 5: "reduced self-esteem and self-confidence"<br> q7 = Is your self-confidence clearly lower than usual?<br> q8 = Are you feeling up to your tasks? </p> <p>ICD-10 symptom 6: "ideas of guilt and unworthiness"<br> q9 = Are you blaming yourself currently? <br> q10 = Do you think you are worth less than others right now?</p> <p>ICD-10 symptom 7: "bleak and pessimistic views of the future"<br> q46 = Are you thinking that you will be doing well in the future? <br> q47 = Are you looking hopefully into the future?</p> <p>ICD-10 symptom 8: "ideas or acts of self-harm or suicide"<br> q16 = Are you thinking about death more often than usual? </p> <p>ICD-10 symptom 9: "disturbed sleep"<br> q13 = Did you sleep badly last night? </p> <p>ICD-10 symptom 10: "diminished appetite"<br> q14 = Do you have less or no appetite today? </p>
Linked collectors and determiners for: Taxonomy and Biogeography without frontiers – WhatsApp, Facebook and smartphone digital photography let citizen scientists in more remote localities step out of the dark.
Natural history specimen data linked to collectors and determiners held within, "Taxonomy and Biogeography without frontiers – WhatsApp, Facebook and smartphone digital photography let citizen scientists in more remote localities step out of the dark". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698">https://bionomia.net/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698">https://gbif.org/dataset/d9953b25-4b14-437c-aecf-1ccb60e8d698</a>. Formatted as a Frictionless Data package.
Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour
<p>This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.</p>
Fig. 1 in LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost
Fig. 1. Example of a 3D point-cloud model of specimen habitat obtained with the LiDAR scanner of an iPad Pro. A, Plan view of the model with potential use cases, including annotation and extraction of general habitat characteristics; B, Side view with measurements that can be extracted from the model at centimetre precision (DBH, diameter at breast height); C, Average times needed for physical herbarium specimen collection (orange) and LiDAR scanning (purple) in the field over 20 replicates; time for scanning depends on the area scanned and the habitat.
Data for Manuscript: Instrumental Validity of the Motion Detection Accuracy of a Smartphone Based Training Game
<p><strong>Background: </strong>In the project TRIMOTEP we developed a low-cost augmented reality training game. Aim of the training game ist to support patients after total hip replacement in their rehabilitation. The project was funded by the Austrian Research Promotion Agency (FFG, grant number 862050). As hardware the training game uses a headset, an android smartphone and a step board. The goal of the training game is to dodge animals and objects while performing exercises. A current version of the training game can be downloaded here: https://trimotep.fh-joanneum.at/exer-game-ar_walker/ . The training game is based on Google ARCore and uses a movement detection approach to recognise different exercises. To detect movements ARCore uses the smartphone inbuilt inertial measurement unit and the front camera (https://developers.google.com/ar/discover). In order to investigate the possibilities of the training game, it is necessary to examine the accuracy of movement detection in more detail.</p> <p><strong>Data: </strong>To investigate the accuracy, comparative measurements were carried out with 30 healthy subjects. During the measurements, the subjects motion was recorded simultaneously with the training game and an optoelectronic motion capture system (Vicon). Two trials were recorded with each subject.</p> <p>First Trial: subjects followed a protocol</p> <p>Second Trial: subjects played the training game for one minute</p> <p>The training game measures the movement of the smartphone (and therefore of the headset and the head). The optoelectronic motion capture system uses a marker set consisting of four markers. Those markers are labeled HMD_F, HMD_B, HMD_R, HMD_L. Markers HMD_R and HMD_L as well as HMD_B and HMD_F form an axis in a karthesian coordinate system. This coordinate system is rotated by 8 degrees compared to the training game along the transversal axis.</p> <p><strong>Structure of the Data Set:</strong> The data set includes an excel sheet with general data of the subjects and a figure showing the tilt between the two coordinate systems. Further one folder contains the measurement data of the training game as json files. Another folder contains the measurement data of the optoelectronic motion capturing system as csv files.</p> <p> </p> <p>For further information or help to process the data please contact:</p> <p>Bernhard Guggenberger, bernhard.guggenberger2@fh-joanneum.at</p>
Design and Development of a Smartphone-Based Geolocalized Exposure Therapy Software for Anxiety Disorders: SyMptOMS-ET -- Reproducibility Package
<p>R Notebook and datasets for the submitted paper "<em>Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app</em>"</p> <blockquote> <p>Alberto González-Pérez, Laura Diaz-Sanahuja, Miguel Matey-Sanz, Jorge Osma, Carlos Granell, Juana Bretón-López, Sven Casteleyn. Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app. <a href="https://journals.sagepub.com/home/dhj">Digital Health Journal</a> [Submitted]</p> </blockquote> <p>Experiments were conducted using the v1.2.0 version of the SyMptOMS-ET open-source app, which can be found <a href="https://github.com/GeoTecINIT/symptoms-mobile-app/releases/tag/v1.2.0">here</a>.</p>
Locating undocumented orphaned oil and gas wells with smartphones
<p>Majority of the estimated 3 million abandoned oil and gas wells in the U.S. have missing documents and lack surface equipment making them difficult to locate. However, most of them have casings made of iron alloys which are magnetic and can be sensed by magnetometers. Here we utilize an iPhone 12 mini smartphone as a magnetometer to locate two abandoned wells. We designed a simple unmanned aerial vehicle (UAV) survey setup where the iPhone 12 mini was hung from an inexpensive small drone. We surveyed the two sites by flying the drone at altitudes, 10 m, 15 m, and 20 m above ground level. Our results show that at altitude of 10 magl the smartphone magnetometer could pick the magnetic anomaly of either of the wells at intensities ≥ 52 μT; sufficient to accurately locate the wells. At altitude of 15 magl the smartphone could locate the wells within ~5 m radius of the actual wells’ location, and it was unable to detect any magnetic anomalies at 20 magl. Simplicity of the setup, minimal required scientific knowledge and low cost of the setup makes this setup an ideal tool for locating orphaned wells by citizen scientists.</p>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Building an Ecological Momentary Assessment Smartphone App for 4- to 10-Year-Old Children: A Pilot Study
<p>The file "KIDsRawData_0111.RData" is a R data file, in which the "KIDs_data" dataframe contains EMA survey responses of all participants during the study period. </p> <p>The file "iEMA Post-study Caregiver Survey.xlsx" contains caregivers' responses to post-study survey on app usability and other.</p> <p>The file "Call Log and Reasons for not Participating.xlsx" is a list of call (to caregivers) logs. </p>
Commodifying infrastructure spatial dynamics with crowdsourced smartphone data
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Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.