Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation. Methods The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.

All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.

The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.

Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.

This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.

This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.

The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.

This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.

A large-scale mobile typing dataset contains 46 755 participants typing sentences in English and 8661 participants in Finnish on their own mobile devices. Participants used various iPhone and Android devices with different operation system versions. The data was collected between 2019 and 2020 by the Computational Behaviour Lab of Aalto University. The user's typing operations and use of Intelligent Text Entry (ITE) methods (Autocorrection and Suggestion Bar) are labelled on a keystroke level. The dataset enables analysis of the effects of the user demographics and the usage and accuracy of ITE methods on typing. The dataset also has a separate table for all ITE corrected and predicted words e.g. for the ITE error analysis.

Code repository: https://github.com/aalto-speech/ite-typing-dataset/

Citation:

Leino, Katri, Markku Laine, Mikko Kurimo, and Antti Oulasvirta. Mobile Typing with Intelligent Text Entry: A Large-Scale Dataset and Results. 2024. https://doi.org/10.21203/rs.3.rs-4654512/v1

Casual smartphone 3D scans using three devices: iPhone 15 Pro, Samsung Galaxy S20 FE and Google Pixel 5. The raw data (prefixed "spectacular-rec-") has been captured with the Spectacular Rec applications for Android and iOS, and contains time-synchronized video and IMU data. The extras file also contains AprilGrid calibration sequences, as well as pre-computed calibration results, for the Android devices.

The data is captured in non-ideal lighting conditions and has a moderate amount of motion blur and rolling shutter artefacts. The included metadata also contains the exposure times and the (Android) rolling shutter readout times, as well as the built-in calibration data, as reported by the devices.

The dataset also contains three different processed variants (prefixed with "colmap-"), which are directly trainable with Nerfstudio. In the processed variants, suitable minimally blurry video frames have been selected as key frames and their poses have been registered with COLMAP. In addition, the local linear and angular velocities of each key frame has been estimated using VIO with Spectacular AI Mapping Tools. The "calib-intrinsics" and "orig-intrinsics" variants include manually calibrated and built-in intrinsics, respectively. They depend on the third variant with symbolic links. The third variant has COLMAP-estimated intrinsics, which are relatively inaccurate for the Android data with high levels rolling shutter deformation.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web.
It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website.
It has detailed information such as when an individual is walking, driving, and flying.
Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website.
Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download.
The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user.
The dataset comprises a pair of (latitude, and longitude), and a timestamp.
All the data was delivered in a single CSV file.
As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020.
10.1007/s12243-020-00807-x (DOI)
and
T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.
10.1111/exsy.12627 (DOI)

Percentage of smartphone users by selected smartphone use habits in a typical day.

Infant Crying Smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Infant Crying smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Global Cloud Mobile Backend as a Service (BaaS) Market size was $3.0 Billion in 2022 and is slated to hit $7.3 Billion by the end of 2030 with a CAGR of nearly 24.1%.

The dataset to be published was generated through exploratory case studies conducted on wrist-worn devices from three vendors: Huawei, Amazfit, and Xiaomi. The specific devices investigated include the Huawei Fit 2 Smartwatch and Band 7, Amazfit Band 7, and Xiaomi Watch 3. These devices operate on different operating systems, namely Android Wear, Zepp OS, and Wear OS.

The data collection period for each device varies, with Huawei having approximately one year of data collected, while the other devices have shorter durations. All wrist-wear devices from different vendors were connected to an iPhone 11 mobile device, which acted as the host device. The iPhone facilitated data synchronization and provided access to the data through the respective health applications provided by the vendors.

To extract the data, MD-NEXT was employed, and the extracted data was further analyzed using the MD-RED tool. These tools were chosen due to their recognized forensically sound capabilities. As a result, the dataset contains data that is considered suitable for use in digital forensics fields.

Overall, the dataset provides valuable information obtained from wrist-worn devices, covering multiple vendors, operating systems, and data collection periods. Researchers in the digital forensics field can utilize this dataset for various investigative and analytical purposes.

Differences between operating systems (Android, iOS, Mac OS, and Windows; Study 2).

ChatGPT ChatGPT

Poe

The dataset to be published was generated through exploratory case studies conducted on wrist-worn devices from three vendors: Huawei, Amazfit, and Xiaomi. The specific devices investigated include the Huawei Fit 2 Smartwatch and Band 7, Amazfit Band 7, and Xiaomi Watch 3. These devices operate on different operating systems, namely Android Wear, Zepp OS, and Wear OS.

The data collection period for each device varies, with Huawei having approximately one year of data collected, while the other devices have shorter durations. All wrist-wear devices from different vendors were connected to an iPhone 11 mobile device, which acted as the host device. The iPhone facilitated data synchronization and provided access to the data through the respective health applications provided by the vendors.

To extract the data, MD-NEXT was employed, and the extracted data was further analyzed using the MD-RED tool. These tools were chosen due to their recognized forensically sound capabilities. As a result, the dataset contains data that is considered suitable for use in digital forensics fields.

Overall, the dataset provides valuable information obtained from wrist-worn devices, covering multiple vendors, operating systems, and data collection periods. Researchers in the digital forensics field can utilize this dataset for various investigative and analytical purposes.

The SYRCityline requests is a very large dataset This includes requests for city services which have been made by residents through SYRCityline, which utilizes SeeClickFix software. Service requests can be made at:https://seeclickfix.com/syracuseData DictionaryCreated at local - When this complaint or service was requested. (This is also in the format of MM/DD/YYYY - HH:MM(AM/PM)).Address - Address of the service request or complaint, provided by the community member.Summary - String that users select to categorize the nature of their complaint. Can be either Large or Bulk Items, Illegal Setouts, Sewer Back-ups, Weekly Trash Pickup, Large or Bulk Items - Skipped Pickup, Home & Building Maintenance, Sewer-related Concerns, Recyclling, Other Housing & Property Maintenance Concern, Streeth Lights, or Other.Rating - The number of followers on the Request in SeeClickFix.Description - Write up of the service request or complaint, provided by the community member.Agency Name - What type of City Department was this complaint assigned to. These include:Streets, Sidewalks & TransportationGarbage, Recycling & GraffitiHousing & Property MaintenanceFeedback to the CityParking & VehiclesGreen Spaces, Trees & Public UtilitiesWater & SewageAnimalsURL - Unique website address (url) that the complaint as well as comments from the City personnel can be viewed at.Latitude - Latitude GPS coordinate where the address is.Longitude - Longitude GPS coordinate where the address is.Export tagged places - Which quadrant of the city is this address matched to (Northeast, Southeast, Northwest, or Southwest).Acknowledged at local - When this complaint or service request was acknowledged by the City department.Closed at local - When this complaint or service request was marked as being closed by the City department.Minutes to acknowledged - The amount of time, in minutes, after it was Created at Local to being marked Acknowledged at local.Minutes to closed - The amount of time, in minutes, after service request was created at local to when it was marked as Closed at local.Assignee name - Which city Department was assigned to this request.Category - How was this request categorized. This can be Potholes, Large or Bulk Items, Water-related Concerns, Home & Building Maintenance, Street Lights, Weekly Trash Pickup, Public Trash Can, Yard Waste, Report Litter on Private Land, among other categories.SLA Limit - This is the limit assigned by the City of Syracuse, that puts a limit on how a request can stay in the list of tasks untouched. That amount of time, in hours, SeeClickFix will forward the request to the department head as well as an administrator to help ensure that requests are addressed in a timely manner.Report source - How this service request was obtained: Either Web-Mobile, iPhone, Portal, Web-Desktop, Android, or Request Form.Dataset OwnerOrganization: Department of Public Works (DPW)Position: Data Program ManagerCity: Syracuse, NYE-Mail Address:opendata@syrgov.net

General Information It collects 799 Japanese locals and is recorded in quiet indoor places, streets, restaurant. The recording includes 210,000 commonly used written and spoken Japanese sentences. The error rate of text transfer sentence is less than 5%. Recording devices are mainstream Android phones and iPhones.

Content 799 people 21,000 sentences 16kHz, 16bit, wav

Acknowledgements Original location: https://bit.ly/3a6KZua

License Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing

Descriptives of study 1 variables separated for operating systems (Android, iOS).

What’s Included

Introducing the English Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the English language.

Containing a total of 2000 images, this English OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible English text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native English people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the English language. Your journey to enhanced language understanding and processing starts here.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of mobile broadband connections in countries like Vietnam and Laos.

The smartphone penetration in the Philippines was forecast to continuously decrease between 2024 and 2029 by in total 6.4 percentage points. According to this forecast, in 2029, the penetration will have decreased for the fourth consecutive year to 65.75 percent. The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like Laos and Malaysia.