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.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion 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 the Americas and Asia.

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

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion 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 Australia & Oceania and Asia.

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

China is leading the ranking by number of smartphone users , recording 859.38 million users. Following closely behind is India with 700.58 million users, while Seychelles is trailing the ranking with 0.05 million users, resulting in a difference of 859.33 million users to the ranking leader, China. 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).

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_65567444c3df02aceb795897bbd183c9/view

The number of smartphone users in Ireland was forecast to continuously increase between 2024 and 2029 by in total 0.3 million users (+6.15 percent). After the seventh consecutive increasing year, the smartphone user base is estimated to reach 5.22 million users and therefore a new peak 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 information concerning Serbia and Sweden.

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.
All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors:
Sensors:
This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are:- Average of accelerometer and gyroscope values.- Maximum and minimum of accelerometer and gyroscope values.- Variance of accelerometer and gyroscope values.- Peak-to-peak (max-min) of X, Y, Z coordinates.- Magnitude for gyroscope and accelerometer.

Statistics:
These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day.- Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one.- ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces.

Voice:
This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding.


A summary of the details of the collected database.
- Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

This dataset consists of aggregated statistics that enable to reproduce results for the paper: "Exposure to urban and rural contexts shapes smartphone usage behavior.", published on PNAS Nexus. The data contains smartphone usage for 454,018 individuals. More details about the data are included in the article. Each row represent one smartphone user, and includes the following information

[gender] self-reported gender [age-group] age-group (computed from self-reported age, 1=18-26, 2=27-36, 3=36-48, 4=48-66, 5=66+) [urbanization]: urbanization level around the home location (3=urban, 2=suburban, 1=rural) [GID_0]: country of residence [median_screen_time]: median daily smartphone usage (minutes) [median_n_apps]: median number of unique apps.

For each field x among the following, the value represents the fraction of usage in category x. Categories are assigned by the Android Play Store

[Books] [Browsing] [Business] [Camera/Album] [Communication] [Entertainment] [Game] [Health_and_Fitness] [Maps_and_Navigation] [Movie/TV] [Music] [News] [Other] [Productivity] [Shopping] [Social] [Tools] [Travel_and_Local] [Weather]

Smart phone price index (CPPI) by North American Product Classification System (NAPCS). The table includes annual data for the most recent reference period and the last four periods. Data are available from January 2015. The base period for the index is (2015=100).

The number of smartphone users in Norway was forecast to continuously increase between 2024 and 2029 by in total 0.3 million users (+5.89 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 5.39 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 further information concerning Denmark and Sweden.

This data set is supplement to this Scientific Reports article.

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

Metrics:

Estimated daily average travelled distance by people.

Estimated percentage of people who did not move during the 24 hours of the day.

Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

Covered period: from March 11, 2020 to present.

Temporal resolution: daily.

Temporal smoothing:

No smoothing.

7-day moving average.

14-day moving average.

21-day moving average.

28-day moving average.

Uncertainty: 95% bootstrap confidence interval.

Data ownership

Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

Contribution

Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

Repository update

CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

English(the United States) Scripted Monologue Smartphone speech dataset_Guiding, collected from monologue based on given prompts, covering smart car, smart home, voice assistant domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(344 speakers), 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.

Digitally semi-literate means those people who face challenges in digital enablement and are not too familiar with using smartphones for text message communication. Any progress to reduce the difficulty of their smartphone usage can help these people. These people are over one billion worldwide. The dataset contains text messages in English (some of these are translations of local text messages) from semi-literate Indian users. The dataset has been derived from face to face surveys primarily. Only 10% by online surveys since these people are not comfortable in doing online surveys.

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.

Italian(Italy) Scripted Monologue Smartphone speech dataset, collected from monologue based on given common-used sentences, with balanced gender distribution. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(800 people), 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.

About

The COTIDIANA Dataset is a holistic, multimodal, and multidimensional dataset that captures three dimensions in which patients are frequently impacted by Rheumatic and Musculoskeletal Diseases (RMDs), namely, (a) mobility and physical activity, due to joint stiffness, fatigue, or pain; (b) finger dexterity, due to finger joint stiffness or pain; or (c) mental health (anxiety/depression level), due to the functional impairments or pain.

We release this dataset to facilitate research in rheumatology, while contributing to the characterisation of RMD patients using smartphone-based sensor and log data.

We gathered smartphone and self-reported data from 31 patients with RMDs and 28 age-matched controls, including (i) inertial sensors, (ii) keyboard metrics, (iii) communication logs, and (iv) reference tests/scales. We provide both raw and (pre-)processed dataset versions, to enable researchers or developers to use their own methods or benefit from the computed variables. Additional materials containing (a) illustrations, (b) visualization charts, and (c) variable descriptions can be consulted through this link.

Citing

When using this dataset, please cite P. Matias, R. Araújo, R. Graça, A. R. Henriques, D. Belo, M. Valada, N. N. Lotfi, E. Frazão Mateus, H. Radner, A. M. Rodrigues, P. Studenic, F. Nunes (2024) COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal Diseases, in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 11, pp. 6538-6547, DOI: 10.1109/JBHI.2024.3456069.

Data structure

The data is organised by participant and includes:

• Inertial Sensor Data, retrieved from accelerometer, gyroscope, and magnetometer sensors collected during three distinct walking exercises (Timed Up and Go, Daily Living Activity, and Simple Walk);

• Keyboard Dynamic Metrics, collecting 38 raw variables related with the keyboard typing performance while writing 10 sentences (e.g., number of errors, words-per-minute);

• Communication Logs, e.g., with weekly averages of number of calls and SMS sent or received;

• Validated Clinical Questionnaires, such as general Health (EQ-5D-5L), Multidimensional Health Assessment Questionnaire (MDHAQ), Hospital Anxiety and Depression Scale (HADS);

• Validated Functional Tests, including time to perform the Timed Up and Go (TUG) and Moberg Pick-Up Test (fine motor skills);

• Characterization Questionnaire, containing sociodemographic and clinical information.

cotidiana_dataset
├── info
│ ├── codebook.xlsx
│ ├── missings_report.csv
├── processed
│ ├── com_calls
│ │ └── features.csv
│ ├── com_sms
│ │ └── features.csv
│ ├── full
│ │ └── cotidiana_dataset.csv
│ ├── hd_kst
│ │ └── features.csv
│ ├── hd_mpu
│ │ └── features.csv
│ ├── mob_dla
│ │ └── features.csv
│ ├── mob_sw
│ │ └── features.csv
│ ├── mob_tug
│ │ └── features.csv
│ ├── quest
│ └── features.csv
├── raw
│ ├── com_calls
│ │ └── p[0-58]
│ │ └── calls_log.csv
│ ├── com_sms
│ │ └── p[0-58]
│ │ └── sms_log.csv
│ ├── hd_kst
│ │ └── p[0-58]
│ │ ├── imu
│ │ │ ├── Accelerometer_s[0-9].csv
│ │ │ ├── Gyroscope_s[0-9].csv
│ │ │ └── Magnetometer_s[0-9].csv
│ │ └── keyboard
│ │ └── kb_metrics.csv
│ ├── hd_mpu
│ │ └── p[0-58]
│ │ └── mpu_time.csv
│ ├── mob_dla
│ │ └── p[0-58]
│ │ ├── bag
│ │ │ ├── Accelerometer.csv
│ │ │ ├── Gyroscope.csv
│ │ │ ├── Magnetometer.csv
│ │ │ └── Annotation.csv
│ │ └── pocket
│ │ ├── Accelerometer.csv
│ │ ├── Gyroscope.csv
│ │ ├── Magnetometer.csv
│ │ └── Annotation.csv
│ ├── mob_sw
│ │ └── p[0-58]
│ │ ├── ann
│ │ │ └── walk_ann.csv
│ │ ├── bag
│ │ │ ├── Accelerometer.csv
│ │ │ ├── Gyroscope.csv
│ │ │ ├── Magnetometer.csv
│ │ │ └── Annotation.csv
│ │ └── pocket
│ │ ├── Accelerometer.csv
│ │ ├── Gyroscope.csv
│ │ ├── Magnetometer.csv
│ │ └── Annotation.csv
│ ├── mob_tug
│ │ └── p[0-58]
│ │ ├── bag
│ │ │ ├── Accelerometer.csv
│ │ │ ├── Gyroscope.csv
│ │ │ ├── Magnetometer.csv
│ │ │ └── Annotation.csv
│ │ └── pocket
│ │ ├── Accelerometer.csv
│ │ ├── Gyroscope.csv
│ │ ├── Magnetometer.csv
│ │ └── Annotation.csv
│ ├── quest
│ └── features.csv

There are a total of 17 questions in the survey, addressing the following categories:Internet useMobile phone use (smartphones & basic voice/SMS phones)Awareness and use of WikipediaGeneral demographicsThe survey collected 2500 total responses, representing populations in 5 geographical regions served by 3 mobile Iraqi operators. 3 language choices (Arabic, English, Kurdish) were provided.Here are the main questions this survey was designed to answer. However, analyzing the full data set allows you to conduct more in-depth data explorations and gain meaningful insights beyond the points presented here.What is the actual number of people who use the internet?(Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.)For internet users: What do people mostly use the internet for?For non-internet users: Why not use the internet?How many people use smartphones?Do people with smartphones use the internet from just Wifi? Or just cellular service?How many people think that they don’t use the internet, but still use Facebook or WhatsApp?How many people have heard of Wikipedia? What do they use it for? How often?If they have heard of Wikipedia, but aren’t using it, why not?Compared to previous phone surveys in other countries, the 2017 Iraq phone survey presented new questions.What are people’s awareness of other major internet brands in comparison to Wikipedia?Can people find online content in their preferred language?How does data cost impact internet use?