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118 results for “mobile device”
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 11. Children applying weaving techniques under the control of the staff from NUA Bucharest
<p>The information from a) and b) has been used by other colleagues in the local school and in the future will be used by other schools in the country or abroad. This represented the second educational level of the project, i.e. the analysis of the collected data. Information from c) was further analyzed by university teachers, filtered and added as an enhancement to the content of the AR platform. An example of user-created content are the movies made with smartphone cameras, recording the children2 while performing traditional crafts (Figures 11, 12). </p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 8. Functions of the mobile devices used in the practices (Note: Each case could involve the use of more than one function.)
<p>Figure 8 captures the functions of mobile devices used in the practices. The results show that tailor-made applications for specific practices were most common (74%), followed by the use of a speaker (32%) and a camera (26%), where learners had to listen to audio materials using speakers or access online information by scanning QR-codes through cameras. In the various practices, other functions were also used, such as messaging (16%) for interacting with diverse parties and GPS (14%) for outdoor learning activities. For the practices using older models of mobile devices without cameras, tools such as an RFID reader (8%) were used for accessing information via communication tags.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 6. Purposes of using mobile devices for learning
<p>Figure 6 shows the purposes of mobile and ubiquitous learning in the studies. A majority of the cases were multi-purpose (54%). Those with a single specific purpose were focused on “practice or revision” (34%) and “knowledge acquisition” (10%). There was a case where the application was to help new students to become familiar with the campus and teach them about the use of the facilities.</p>
Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls
<p><strong>Dataset description</strong></p> <p>The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.</p> <p><strong>Meta Information</strong></p> <p>The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.</p> <p><strong>Recording Procedure</strong></p> <p>We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:</p> <ul> <li>Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}</li> <li>Ask the participant to read out “The North Wind and the Sun”</li> <li>Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”</li> <li>Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.</li> <li>Test executor ends call by farewell (on-hook signal triggered).</li> </ul> <p><strong>Annotation Scheme</strong></p> <p>For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:</p> <p>SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18</p> <p>with</p> <ul> <li>SI as subject identification in the form ID<em>NN</em>, <em>N</em> in [0, 9]</li> <li>HS as the health status label (hc or pd accordingly)</li> <li>HYR as the expert assessed H&Y scale rating</li> <li>UPDRS II-5 as the according expert peer-reviewed score</li> <li>UPDRS III-18 as the according expert assessed score</li> </ul> <p>For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.</p> <p><strong>Appendix</strong></p> <p>North Wind and the Sun (Orthographic Version):</p> <p>“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”</p> <p>BNC – Tech. Engin. Computer applications in geography snippet:</p> <p>“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”</p>
DynamicRead: Eye Movement Data of Reading on Handheld Mobile Devices under Dynamic Conditions
<p>The DynamicRead dataset contains eye movement data from 20 participants engaged in reading tasks on a handheld mobile device, while sitting and walking. Participants read 10 texts using four gaze interaction methods based on scrolling techniques, as well as one touch-based interaction method. The dataset captures the impact of motion on eye movement and features an unstable eye-movement sampling frequency ranging from 8-12fps/hz, which poses a challenge for traditional eye-movement toolkits. This dataset can be valuable for academic research on the impacts of motion on eye movement and the development of robust gaze interaction methods for handheld mobile devices under dynamic conditions.<br> <br> ###<br> Folder Structure:<br> -'eye_data' -- contains eye movement of reading data<br> -'text_screenshot' -- contains different reading material screenshot<br> -'heatmap_img' -- contains heat map of each reading page <br> -'scanpath_img' -- contains scan path of each reading page<br> -'text' -- contains reading material<br> -'code' -- contains R code for reading eye movement visualisation</p> <p><br> Name Method<br> - GAP1_GazeA_log.txt -- Group A, Participant No1, Scrolling technique: GazeA<br> - Gaze A: Auto-scrolling<br> - Gaze B: Heatbox<br> - Gaze C: Eye-Swipe<br> - Gaze D: Moving-bar<br> </p>
Artifact for MobiCom'23: Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility
<p>This dataset contains the anonymized failure data collected from our physical and device farms over a three-month period. The failure data involves 5,918 physical devices as well as 5,918 virtualized devices running on ARM commodity servers.</p> <p>For more details, please visit our website (<a href="https://android-emulation-testing.github.io/">Android-Emulation-Testing.github.io</a>) or read our paper:</p> <ul> <li>[MobiCom'23] Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility</li> </ul> <p>If you use our dataset in your work, please reference it using:</p> <pre><code>@inproceedings {lin2022virtual, author = {Lin, Hao and Qiu, Jiaxing and Wang, Hongyi and Li, Zhenhua and Gong, Liangyi and Gao, Di and Liu, Yunhao and Qian, Feng and Zhang, Zhao and Yang, Ping and Xu, Tianyin}, title = {{Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility}}, booktitle = {The 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'23)}, year = {2023}, publisher = {ACM} }</code></pre> <p> </p> <p> </p>
Patent Landscape of Mobile Device Technologies in India
<p>This dataset contains a landscape of 23,569 patents and patent applications registered in India and relevant to the domain of mobile technology. These patents and patent applications are held by 50 Indian and non-Indian companies operating in the country. The patent landscape has been released under the Creative Commons-Attribution-Share Alike 4.0 (CC-BY-SA 4.0) License as a part of the Pervasive Technologies research project at the Centre for Internet and Society, India. </p> <p>This study was conducted by Prof Jorge Contreras at the University of Utah, USA, and Rohini Lakshané at the Centre for Internet and Society, India. </p> <p>For the detailed methodology used for drawing up this landscape, refer to: <a href="http://cis-india.org/a2k/blogs/patent-landscaping-in-the-indian-mobile-device-market">http://cis-india.org/a2k/blogs/patent-landscaping-in-the-indian-mobile-device-market</a></p> <p>A paper titled "<a href="https://scholarship.law.vanderbilt.edu/vjtl/vol50/iss1/1/">Patents and Mobile Devices in India: An Empirical Survey</a>" published in the Vanderbilt Journal of Transnational Law (2017) presents an analysis of this patent landscape.</p> <p>For queries regarding the dataset or its reuse, write to rohini dot lakshane at gmail dot com. </p>
Non-cooperative 802.11 MAC layer fingerprinting and tracking of mobile devices
<p>This archive contains the datasets used for the experiments in the paper "Non-cooperative 802.11 MAC layer fingerprinting and tracking of mobile devices", namely:</p> <ul> <li><em>Glimps 2015 dataset (mac_info collection)</em>: A collection of 122,989 Probe Request frames captured by 8 monitoring stations at the Glimps music festival in Ghent, Belgium (10 - 12 December 2015). To minimize overhead, each monitoring station individually stored only one Probe Request per unique MAC. The dataset was used to show that the high entropy in Probe Requests can be used to deanonymize devices that use MAC address randomization. Only the source MAC and Information Elements (IEs) were captured for this purpose. </li> <li><em>Research center 2016 dataset (mac_research collection)</em>: A complete collection of all management and control frames (including Radiotap headers) observed at our research lab from 28 January to 8 Febuary 2016. This dataset was used to calculate the "stability" and "variability" of Probe Request IEs (see our paper for more details on these metrics).</li> <li><em>Transmission rate datasets (mac_research_0 - mac_research_4 collections)</em>: Observations of mobile devices when actively instigated for extra transmissions. These observations were used in the paper to calculate the effectiveness of the various stimulus frame techniques. This dataset should only be used to verify the results in the paper. The other datasets could be used for related experiments.</li> </ul> <p>All datasets were anonymized by applying the following rules:</p> <ul> <li>The 3 least significant bytes of each MAC address were uniquely and consistently mapped to a different value, with exception of "ff:ff:ff" and "00:00:00".</li> <li>The SSID IE has its SSID field replaced with the string "Hidden", with exception of the wildcard (empty) SSID.</li> <li>The Vendor Specific WPS IE was replaced with a hash of its payload given the amount of sensitive information (device serial / model number, UUID, etc.) contained within it, and the length of the IE was updated accordingly. Unfortunately, Wireshark stops parsing the remainder of Probes containing this anonymized IE, so it should be noted that further parsing beyond the WPS IE must be done manually (e.g. by using Scapy or by changing the Wireshark dissector).</li> </ul> <p>The datasets are provided as MongoDB collections with the following document format:</p> <ul> <li>_id: ObjectID of the document</li> <li>info_length: Length of the binary blob</li> <li>info: Binary blob of the Radiotap frame (mac_research) or only the Information Elements (mac_info)</li> <li>mac_addr: Transmitter of the frame</li> </ul> <p>To install the dataset, execute the command "mongorestore --gzip -d anonymized ./anonymized" after extracting the .tar.xz file.</p> <p>A .pcap format of the <em>mac_info </em>(wrapped in a dummy Radiotap frame) and <em>mac_research</em> datasets is additionally provided at crawdad.org.</p> <p>The <em>mac_info</em> dataset can be visually explored on https://wicability.net/datasets (Glimps 2015 dataset).</p>
COVID-19 contact rates between mobile devices in Connecticut
<p>Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the large initial wave of infections during March–April, the drop in cases during June–August, local outbreaks during August–September, broad statewide resurgence during September–December, and decline in January 2021. The transmission model exhibits a better fit to COVID-19 transmission dynamics using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.</p>
User Study Data for "HapticLock: Eyes-Free Authentication for Mobile Devices"
<p>User study data from the HapticLock paper published in the Proceedings of the ACM ICMI 2021 conference.</p>
Robotic Walking Device to Improve Mobility in Parkinson's Disease
ClinicalTrials.gov study NCT03751371. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Effects of Mobility Devices on Nursing Compliance With Mobility Protocols
ClinicalTrials.gov study NCT01690910. IPD Sharing: NO. Countries: 1. Publications: 9.
Mobile Device Biomonitoring to Prevent and Treat Obesity in Underserved Youth
ClinicalTrials.gov study NCT02017223. IPD Sharing: NO. Countries: 1. Publications: 6.
Stress-physiology Coherence, Interoception, and Well-being Following Mindfulness Training or Tracking Time Spent on Mobile Device
ClinicalTrials.gov study NCT04125758. IPD Sharing: YES. Countries: 1. Publications: 19.
Use of Mobile Devices and the Internet to Streamline an Asthma Clinical Trial
ClinicalTrials.gov study NCT02061280. IPD Sharing: YES. Countries: 1. Publications: 3.
COVID-19 contact rates between mobile devices in Connecticut
Open the record for dataset details and reuse information.
Data from: physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: an intensive, longitudinal dataset
Open the record for dataset details and reuse information.
The DSI dataset for Wi-Fi fingerprinting using mobile devices
<p>The provided data were collected at the first floor of building 11 of the University of Minho, Portugal, Azurém Campus (Department of Information Systems – DSI), between 2 and 3 of May 2016.</p> <p>Data was collected by a single person, using a Nvidia Shield tablet running Android 6.0.</p> <p> </p>
Using mobile device built-in microphones to monitor bats: A new opportunity for large-scale participatory science initiatives
<p>Here we present audio data collected in the study titled "Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives", now accepted for publication in Biodiversity and Conservation (DOI: 10.1007/s10531-024-02818-9).</p> <p>Citizen science has become a crucial tool in biodiversity monitoring, often facilitated by the diffusion of mobile devices, such as smartphones and tablets. High costs of professional equipment often limit large-scale monitoring, particularly in bat monitoring programmes based on acoustic surveys. Here we present the potential of using mobile devices for bat monitoring, allowing for large-scale, volunteer-based monitoring programmes. We initially compared mobile devices' performance with a professional bat detector for recording low-frequency bat calls. We then conducted a citizen science pilot study to test the method's feasibility in a real-world setting, recording echolocation and social calls of nine European bat species. We found high similarity in spectrogram quality between calls recorded by mobile devices and professional bat detectors. However, differences in sound quality and effectiveness among mobile device brands and models were found. The citizen science pilot study tested 35 mobile device models, all of which effectively recorded bats. This study suggests that mobile devices could be an accessible, no-cost tool for large-scale bat monitoring. Incorporating mobile devices into existing monitoring networks or creating new dedicated programmes could not only enhance data collection, but also boost public knowledge and awareness about bats, ultimately promoting informed decision-making and better conservation strategies.</p> <p>To facilitate a comprehensive evaluation of the acoustic data quality achievable through the described method, we are providing access to all bat recordings collected via mobile devices during the study. These have been compiled into the compressed file named "mobile_device_recs.zip".</p> <p>For those seeking a quicker review, we recommend downloading the "Appendix_S6_recs.zip" file, which contains selected examples of echolocation or social calls from the nine bat species recorded during our research. Spectrograms of these example recordings are displayed in Appendix S6 of the published study.</p>
Usability, Acceptability, User Experience, Human-Device Interaction, and Ergonomics in Two Mobile FES-Cycling Systems for Individuals with Spinal Cord Injury
<p>This database originates from a study comparing two FES-cycling systems: the <strong>commercial BerkelBike Pro</strong> and a <strong>recumbent FES-bike prototype</strong> developed by the team at Politecnico di Milano. The aim of the study was to evaluate and compare the <strong>usability</strong>, <strong>acceptability</strong>, <strong>user experience</strong>, <strong>human-device interaction</strong>, and <strong>ergonomics</strong> of these two devices for individuals with spinal cord injury (SCI).</p> <p>The study involved 15 participants with SCI, covering a wide range of ages (18 to 65 years) and injury types (both complete and incomplete, at acute and chronic phases). Each participant underwent three sessions with both FES-cycling devices:</p> <ul> <li><strong>First session</strong>: Dedicated to setting up the system for each participant. For both the BerkelBike Pro and the FES-bike prototype, the system settings were tailored to meet individual needs, ensuring optimal comfort and function.</li> <li><strong>Second and third sessions</strong>: Focused on actual training, where participants used the devices to engage in cycling activities.</li> </ul> <p>At the end of the <strong>third session</strong>, participants completed a series of questionnaires to assess the usability, acceptability, user experience, and ergonomics of both devices. These questionnaires form the core of the study’s data collection and are central to understanding participants' interactions with the FES-cycling systems.</p> <p>The database is organized as follows:</p> <ol> <li><strong>Demographic data</strong>: The first page contains demographic information, including participants' age, gender, height, weight, time after injury, injury type (ASIA grade), and injury level.</li> <li><strong>PGWBI scores</strong>: The second page includes baseline data collected using the <strong>Psychological General Well-Being Index (PGWBI)</strong>, which assesses the participants' emotional and psychological state before engaging with the devices. The PGWBI consists of 22 questions grouped into 6 items: "anxiety", "depression", "positivity", "self-control", "health" and "vitality”. The responses are assessed on a 6-point scale ranging from 0 to 5. The score contributions for each question are then added together and transformed to achieve the final score that can range from 0 to 110.</li> <li><strong>SUS scores</strong>: The third page contains the results from the <strong>System Usability Scale (SUS)</strong>, a standard 10-item questionnaire that evaluates the usability of each FES-cycling system based on user ratings. The SUS is evaluated using a 5-point Likert scale, where 1 corresponds to strongly disagree, while 5 to strongly agree. The score contributions for each question are then added together and multiplied by 2.5 to achieve the final score that can range from 0 to 100, where higher scores indicate better usability.</li> <li><strong>TAM-3 scores</strong>: The fourth page presents data from the <strong>Technology Acceptance Model 3 (TAM-3)</strong>, which measures how participants perceive the ease of use and the usefulness of the devices. . The TAM-3 consists of 50 questions grouped into 14 items. These items include “perceived usefulness”, “perceived ease of use”, “self-efficacy”, “perception of external control”, “playfulness”, “anxiety”, “enjoyment”, “subjective norm”, “voluntariness”, “image”, “relevance”, “output quality”, “result demonstrability” and “behavioral intention”. The items are investigated using a 7-point Likert scale, where 1 corresponds to strongly disagree, while 7 to strongly agree.</li> <li><strong>UEQ scores</strong>: The fifth page includes responses from the <strong>User Experience Questionnaire (UEQ)</strong>, evaluating participants' experience with the devices. The UEQ consists of 26 questions grouped in six items: “attractiveness”, “perspicuity”, “efficiency”, “dependability”, “stimulation” and “novelty”. Questions are scored using a 7-point Likert scale, where 1 corresponds to strongly disagree, while 7 to strongly agree. Then scores per item are transformed using a scale ranging from -3 to +3, with +3 representing the most positive value (extremely good) and -3 the most negative one (horribly bad). Values between -0.8 and 0.8 represent a neutral evaluation of the corresponding scale, values > 0.8 represent a positive evaluation and values < -0.8 represent a negative one.</li> <li><strong>Custom questionnaire scores</strong>: The sixth page contains data from a <strong>custom-designed questionnaire</strong>, created specifically for this study to assess the ergonomics of the two FES-cycling systems, with particular attention to human-device interaction at both the physical and psychological levels. It consists of 12 questions covering four items: the transfer from/to the bikes and the initial tuning, bike comfort, its accessibility, and the ease of interaction. Questions are evaluated using a 5-point Likert scale, where 1 corresponds to strongly disagree/very uncomfortable, while 5 to strongly agree/very comfortable.</li> </ol> <p>This comprehensive data collection allows for a thorough comparison of the two FES-cycling systems in terms of user experience and overall acceptability in the SCI population.</p> <p>Please cite the following manuscript when using this database:<br>Nossa R, Biffi E, Sanna N, Diella E, Guanziroli E, Ferrari F, Ferrante S, Molteni F, Pedrocchi A, Tarabini M, Ambrosini E. Assessment of User Experience, Acceptability, Usability, Human-Device Interaction, and Ergonomics in Two Mobile FES-Cycling Systems for Individuals With Spinal Cord Injury. Artif Organs. 2025 Apr 16. doi: 10.1111/aor.15007. Epub ahead of print. PMID: 40237144.</p>
ScienceDex guides
Understand access before you commit
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.