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.

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.

People_with_smartphone

## Overview

People_with_smartphone is a dataset for object detection tasks - it contains Person With Phone annotations for 1,010 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** 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).

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

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.

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.

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.

Thai(Thailand) 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(490 native 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.

This dataset contains transcribed fieldnotes from a “methodological assemblage” and technological prototype connecting autoethnography to the practices of self-research in personal science. As an experimental process of personal data gathering, the author used a low-tech device for the active registration of events and their perception, in a case study on disengaging from his smartphone.

The dataset contains 267 data points (timestamp and intensity/duration of perceived events) with alternative classification and subclassification categories through thematic analysis, as well as 112 "on spot" fieldnotes and 112 related retrospective interpretation notes (with corresponding references where indicated) from a focused autoethnography intervention, tracking the authors' experience of living without constant access to a smartphone for a month (during May 2021).

For more info on the process and the main results of this personal experiment, here's the derived publication: Senabre Hidalgo, E., & Greshake Tzovaras, B. (2023). “One button in my pocket instead of the smartphone”: A methodological assemblage connecting self-research and autoethnography in a digital disengagement study. Methodological Innovations. https://doi.org/10.1177/20597991231161093

As described in the paper, this dataset contains a classification of events based on two complementary approaches:

Initial categories (May 2021): Positive - Negative - Reflection

Retrospective categories and subcategories (November 2021):

Response to habit:

Basic impulse: looking for smartphone in pocket / “If I had it now…”

Abstinence reaction: attempts to reconnect to “the cloud”

Fear of missing out (FOMO) after long periods of time

Disruption in basic routine: perception of altered habits

Social context:

Feelings of embarrassment: in relation to others due to feature phone

Observing others: people using smartphones around me

Meta level: how to share process afterwards (in community, academically)

User experience (UX) practicalities:

Moving around solo: orientation when commuting without GPS

Parasitic use of other smartphones (in case of urgent need)

Capturing things on the spot (pictures, information, feelings)

Local stored culture (music and reading away from screens)

Communication problems: dysfunctions in everyday coordination

Reconnection need: whenever ending up using the smartphone again

Self-reinforcing / awareness:

Succeeding in intended detox: satisfaction for achievement

Full attention to moment / stimulus: mindfulness and concentration

Relevant changes in routine: positive reinforcement by new habit

Contains video data of ASL signs from PopSign v1.0 game category (test set only).

Video data before landmark data as part of the 2023 Google - Isolated Sign Language Recognition Competition https://www.kaggle.com/competitions/asl-signs See related landmark data here: https://www.kaggle.com/competitions/asl-signs/data

Information below mirrored from original source: https://signdata.cc.gatech.edu/view/datasets/popsign_v1_0

PopSign ASL v1.0

95% of deaf children are born to hearing parents. Since many hearing parents do not know sign, these deaf children are at risk for language acquisition delays resulting in cognitive issues. We are making an educational smartphone game PopSign that helps hearing parents practice their signing vocabulary.

Our dataset is the largest collection of isolated sign videos collected using mobile phones. We are using the data to train recognition models for use in smartphone applications, including the PopSign game. PopSign and related educational technology teach hearing parents and deaf children to sign, reducing developmental problems.

From original paper:

PopSign ASL v1.0 collects examples of 250 isolated American Sign Language signs using the selfie camera on Pixel 4A smartphones in a variety of environments. It is the largest isolated sign language dataset publicly available, the first to focus on one-handed signing with smartphones, and one of the few of its size that has been manually reviewed.

Papers

• PopSign v1.0 Paper
• PopSign v1.0 Supplemental Paper

Swedish Scripted Monologue Smartphone Speech Dataset, collected from monologue based on given scripts. Transcribed with text content. Our dataset was collected from extensive and diversify speakers(1,339 people in total, from Sweden), 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.

Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.

Introduction: It has been 4 months since the discovery of COVID-19, and there have been many measures introduced to curb movements of individuals to stem the spread. There has been an increase in the utilization of web-based technologies for counseling, and for supervision and training, and this has been carefully described in China. Several telehealth initiatives have been highlighted for Australian residents. Smartphone applications have previously been shown to be helpful in times of a crisis. Whilst there have been some examples of how web-based technologies have been used to support individuals who are concerned about or living with COVID-19, we know of no studies or review that have specifically looked at how M-Health technologies have been utilized for COVID-19.Objectives: There might be existing commercially available applications on the commercial stores, or in the published literature. There remains a lack of understanding of the resources that are available, the functionality of these applications, and the evidence base of these applications. Given this, the objective of this content analytical review is in identifying the commercial applications that are available currently for COVID-19, and in exploring their functionalities.Methods: A mobile application search application was used. The search terminologies used were “COVID” and “COVID-19.” Keyword search was performed based on the titles of the commercial applications. The search through the database was conducted from the 27th March through to the 18th of April 2020 by two independent authors.Results: A total of 103 applications were identified from the Apple iTunes and Google Play store, respectively; 32 were available on both Apple and Google Play stores. The majority appeared on the commercial stores between March and April 2020, more than 2 months after the first discovery of COVID-19. Some of the common functionalities include the provision of news and information, contact tracking, and self-assessment or diagnosis.Conclusions: This is the first review that has characterized the smartphone applications 4 months after the first discovery of COVID-19.