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.

Apple takes a standard 30 percent commission rate for App Store transactions and regular subscriptions. Additionally, app developers that host their apps on the Apple App Store and manage to retain subscription users see their commission fees drop to 15 percent after the subscriber's first year. After incurring penalties and fines from the Netherlands Authority for Consumers and Markets (ACM), Apple further agreed to decrease commission fees for dating apps hosted in the Dutch Apple App Store. In August 2024, Apple changed its app store policy for the European Union, after receiving a penalty for breaking the EU Digital Markets Act (DMA). App publishers in the EU will be subjected to a standard 17 percent commission fee on in-app purchases made via iOS and iPadOS. Additionally, Apple applies a "Core Technology Fee" of 0.50 Euros for each new app installation after the first million installs.

App publishers and developers In 2021, the leading app distribution platforms – the Apple App Store, the Google Play Store, and the Amazon AppStore- introduced tailored programs to accommodate smaller developers and reduce their due commissions. These program also interested subscription apps, which can now enjoy a 15 percent commission fee on the Google Play Store, and of 15 percent in the Apple App Store after the first 12 months. iOS subscription apps had a conversion rate of approximately 4.55 percent in April 2023, while gaming apps had a commission rate of around two percent in the same month. App stores vs. publishers: revenues During the second quarter of 2024, the Apple App Store was reported to generate 24.6 billion U.S. dollars in revenues from global users, more than double the revenues Android users generated via the Google Play Store. The Apple App Store is expected to generate around 125 billion U.S. dollars in revenues from global consumers worldwide in 2027 while the Google Play Store could reach 60 billion U.S. dollars in revenues from subscriptions and other in-app purchases in the same year. In 2023, seven mobile app publishers and almost 20 mobile gaming app publishers generated one billion U.S. dollars in cumulative revenues.

Explore the Indian Smartphone Market Dataset, featuring demographics, brand preferences (iPhone vs Android), pricing, purchase behavior, and usage trends.

This dataset encompasses mobile smartphone application (app) usage, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or surveying to understand the why. iOS and Android operating system coverage.

Tie app usage to web and location events using anonymized PanelistID for omnichannel consumer journey understanding.

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

Methods: The data was collected via verbal survey, students were asked their gender, age and preference of smart phones. Hypothesis: It is hypothesized that the older individuals would prefer androids over iphone because they would be able to maximize the complex features of android and males would also prefer android because of its affordable and durable features. Prediction 1: Females prefer iPhone. Prediction 2: Older students prefer Android.

Social Media Engagement (2025)

This dataset contains 20,000 synthetic social media posts crafted to mimic realistic user activity on a fictional platform. It simulates various user demographics, post content, hashtags, topics, and detailed engagement metrics such as likes, comments, and shares.

Overview

Each record represents a unique social media post made by a user, enriched with features that allow for analysis of trends, behavior, and engagement. The dataset includes:

• User-level information: age, gender, followers, verified status, etc.
• Post-level information: topic, hashtags, media, engagement
• Platform and device data
• Calculated engagement rate

Column Descriptions

Video Dataset - 1,300+ files

The dataset comprises 1,300+ videos of 300+ people captured using mobile phones (including Android devices and iPhone) and webcams under varying lighting conditions. It is designed for research in face detection, object recognition, and event detection, leveraging high-quality videos from smartphone cameras and webcam streams. — Get the data

  Dataset characteristics:

Characteristic Data

Description Each person recorded 4 videos… See the full description on the dataset page: https://huggingface.co/datasets/ud-biometrics/phone-and-webcam-dataset.

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches.

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.

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.

The Mobile Games Dataset is a meticulously curated collection of 100+ top-rated mobile games spanning various genres. This dataset provides a valuable resource for game developers, researchers, and enthusiasts interested in exploring trends and patterns within the mobile gaming industry. Each entry includes the game name, developer, genre, and rating, offering a comprehensive overview of some of the most popular and critically acclaimed mobile games available today.

Column Descriptions:

• Game Name: The title of the mobile game.

  • Type: String
  • Example: "Candy Crush Saga"

• Developer: The name of the company or individual who developed the game.

  • Type: String
  • Example: "King"

• Genre: The category or type of game, indicating the primary gameplay mechanics.

  • Type: String
  • Example: "Puzzle"

• Rating: The average user rating of the game, typically on a scale from 1 to 5.

  • Type: Float
  • Example: 4.6

This dataset encompasses mobile app usage, web clickstream and location visitation behavior, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). The only omnichannel meter at scale representing iOS and Android platforms.

The FourGroceries dataset is collected for research purposes on price detection analysis. This dataset was collected from four groceries in Turkey in 2022 by mobile phones with IOS or Android operating systems. The dataset consists of 84 images of shelf labels, 21 images from each grocery.

Barcodes and QR codes are images with black and white components that, when scanned, can be coded to transmit information. Barcodes are machine-readable and often used in retail, logistics, manufacturing, point-of-sale, event ticketing, shipping, delivery, and asset management use cases.

This dataset uses bounding boxes for detection of barcodes and QR codes. Once a barcode is identified within an image, an application could trigger the reading of the barcode or post-processing can be used to crop the barcode to be passed for an application to read the barcode.

Barcodes can be read with computer vision using mobile devices such as iPhones or Android devices.

Research available on barcode recognition: https://link.springer.com/chapter/10.1007/978-3-030-57058-3_34

Github resources for barcode recognition: https://github.com/abbyy/barcode_detection_benchmark

How QR codes work: https://typefully.com/DanHollick/qr-codes-T7tLlNi

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. 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 Thailand and Indonesia.

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.

Overview

The MSCardio Seismocardiography Dataset is an open-access dataset collected as part of the Mississippi State Remote Cardiovascular Monitoring (MSCardio) study. This dataset includes seismocardiogram (SCG) signals recorded from participants using smartphone sensors, enabling scalable, real-world cardiovascular monitoring without requiring specialized equipment. The dataset aims to support research in SCG signal processing, machine learning applications in health monitoring, and cardiovascular assessment.

See the GitHub repository of this dataset for the latest updates: https://github.com/TaebiLab/MSCardio

Background

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. SCG is a non-invasive technique that captures chest vibrations induced by cardiac activity and respiration, providing valuable insights into cardiac function. However, the scarcity of open-access SCG datasets has been a significant limitation for research in this field. The MSCardio dataset addresses this gap by providing real-world SCG signals collected via smartphone sensors from a diverse population.

Data Description

Study Population

• Total participants enrolled: 123
• Participants who uploaded data: 108 (46 males, 61 females, 1 unspecified)
• Age range: 18 to 62 years
• Total recordings uploaded: 515
• Unique recordings after duplicate removal: 502
• Platforms used: iOS and Android smartphones

Signal Data

• Axial vibrations in three directions (SCG) recorded using smartphone sensors
• Sampling frequency varies depending on the device capabilities
• Data synchronization is ensured for temporal accuracy
• Missing SCG data identified in certain recordings, addressed through preprocessing

Metadata

Each recording includes:

• Device model (e.g., iPhone Pro Max)
• Recording time (UTC) and time zone
• Platform (iOS or Android)
• General demographic details (gender, race, age, height, weight)

File Structure

The dataset is organized as follows:

MSCardio_SCG_Dataset/
│── info/
│ └── all_subject_data.csv # Consolidated metadata for all subjects
│── MSCardio/
│ ├── Subject_XXXX/ # Subject-specific folder
│ │ ├── general_metadata.json # Demographic and device information
│ │ ├── Recording_XXX/ # Individual recordings
│ │ │ ├── scg.csv # SCG signal data
│ │ │ ├── recording_metadata.json # Timestamp and device details

Data Collection Protocol

• Participants placed their smartphone on their chest while lying in a supine position.
• The app recorded SCG signals for approximately two minutes.
• Self-reported demographic data were collected.
• Data were uploaded to the study's cloud storage.

Usage and Applications

This dataset is intended for research in:

• SCG signal processing and feature extraction
• Machine learning applications in cardiovascular monitoring
• Investigating inter- and intra-subject variability in SCG signals
• Remote cardiovascular health assessment
• The Data_visualization.py script is provided for data visualization

Citation

If you use this dataset in your research, please cite:

@article{rahman2025MSCardio,
author = {Taebi, Amirtah{\`a} and Rahman, Mohammad Muntasir},
title = {MSCardio: Initial insights from remote monitoring of cardiovascular-induced chest vibrations via smartphones},
journal = {Data in Brief},
year = {2025},
publisher = {Elsevier}
}

Contact

For any questions regarding the dataset, please contact:

• Amirtahà Taebi and Mohammad Muntasir Rahman
• E-mail: ataebi@abe.msstate.edu, mmr510@msstate.edu
• Biomedical Engineering Program, Mississippi State University

---

This dataset is provided under an open-access license. Please ensure ethical and responsible use when utilizing this dataset for research.

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.