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

This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fb608498b1cf7f70b9a22952566197db6%2FScreenshot%202023-08-02%20003740.png?generation=1690961033930490&alt=media" alt="">

The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.

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

The number of smartphone users in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ***** 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 *** 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 Denmark and Latvia.

this graph was created in R and Canva :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F89427211b425b885997029de576bc555%2Fgraph1.gif?generation=1739130158617622&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F7963143f93af16d50bfa667550fbffbd%2Fgraph2.gif?generation=1739130165946944&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F07cb6ebb5596b36e8ede943ca9b7f9b8%2Fgraph3.gif?generation=1739130173200555&alt=media" alt="">

Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.

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

This dataset was collected from the Smartphone sensors and can be used to analyse behaviour of a crowd, for example, an anomaly.

Dataset Characteristics: Time-Series

Subject Area: Computer Science

Associated Tasks: Classification

Instances: 14221

Dataset Information

For what purpose was the dataset created?

The key purpose of donating this dataset is to provide an opportunity to the research community to use it for further research purposes.

Who funded the creation of the dataset? Muhammad Irfan

What do the instances in this dataset represent? One instance represents a movement patter for a group based activity.

Are there recommended data splits? No.

Has Missing Values? No

Introductory Paper

Title: Anomaly Detection in Crowds using Multi Sensory Information

Author:M. Irfan, L. Marcenaro, and L. Tokarchuk, C. Regazzoni. 2018

Journal: Published in 5th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), Auckland, New Zealand,

Link: https://ieeexplore.ieee.org/document/8639151

Abstract of Introductory Paper

This paper presents, a system capable of detecting unusual activities in crowds from real-world data captured from multiple sensors. The detection is achieved by classifying the distinct movements of people in crowds, and those patterns can be different and can be classified as normal and abnormal activities. Statistical features are extracted from the dataset collected by applying sliding time window operations. A model for classifying movements is trained by using Random Forest technique. The system was tested by using two datasets collected from mobile phones during social events gathering. Results show that mobile data can be used to detect anomalies in crowds as an alternative to video sensors with significant performances. Our approach is the first to detect any unusual behavior in crowd with non-visual data, which is simple to train and easy to deploy. We also present our dataset for public research as there is no such dataset available to perform experiments on crowds for detecting unusual behaviours.

Cite

Citation: Irfan,Muhammad. (2021). Smartphone Dataset for Anomaly Detection in Crowds. UCI Machine Learning Repository. https://doi.org/10.24432/C5Q90H.

BibTeX: @misc{misc_smartphone_dataset_for_anomaly_detection_in_crowds_613, author = {Irfan,Muhammad}, title = {{Smartphone Dataset for Anomaly Detection in Crowds}}, year = {2021}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5Q90H} }

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 smartphone helps workers balance the demands of their professional and personal lives but can also be a distraction, affecting productivity, wellbeing, and work-life balance. Drawing from insights on the impact of physical environments on object engagement, this study examines how the distance between the smartphone and the user influences interactions in work contexts. Participants (N = 22) engaged in two 5h knowledge work sessions on the computer, with the smartphone placed outside their immediate reach during one session. Results show that limited smartphone accessibility led to reduced smartphone use, but participants shifted non-work activities to the computer and the time they spent on work and leisure activities overall remained unchanged. These findings suggest that discussions on smartphone disruptiveness in work contexts should consider the specific activities performed, challenging narratives of ‘smartphone addiction’ and ‘smartphone overuse’ as the cause of increased disruptions and lowered work productivity.

This dataset lists the most popular smartphones of 2023 in India gathered from Flipkart, one of the largest e-commerce platforms in the country.

The dataset can be used to identify which smartphones and price ranges are preferred by users, the impact of discounts, and how ratings vary.

Column Description

1. title - the name of the smartphone + color of the model + memory + RAM
2. price - price of the smartphone after discount
3. prod_rating - rating of the smartphone
4. rating_count - the number of people
5. discount - the discount offered in percentage
6. seller_rating - rating of the seller of that particular smartphone as rated by their buyer on the whole seller experience

Project Ideas

1) Extract information from the title like brand name, model, color, memory, and RAM. Use different strategies and see which works the best.

2) Correlation analysis - the price of the smartphone could be influenced by rating, number of ratings, discount, and seller rating.

3) Regression - build a regression model to predict the price of a smartphone, by using variables such as "prod_rating," "rating_count," "discount," and "seller_rating" as independent.

4) Visualizations - Get creative with visualizations, create an interactive dashboard, and create forecast charts.

Check out my other dataset on top-rated TV shows: https://www.kaggle.com/datasets/titassaha/top-rated-tv-shows

I write articles on data analysis and analytics, techniques, and document my learning process on my blog - https://emptyjar.in

Thanks.

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.

Our organization provides technology websites with content about the current value of used smartphones and the value per dollar of phones in the market to help people buy and sell smartphones. A model that could predict the real value of phones based on several factors would be helpful.

Attributes of the dataset include:

• PhoneID The identification number of a phone in this dataset.
• Brand The smartphone manufacturer (e.g., Apple, Google, Samsung, etc.)
• Model The smartphone model (e.g., iPhone 13, Galaxy S21, etc.)
• Release Year The year the smartphone model was released.
• Screen Size (inches) The model's screen size is in inches.
• RAM (GB) The model's RAM in GB (2, 4, 6, 8, 12, 16)
• Storage (GB) The internal storage of the smartphone in GB (32, 64, 128, 256, 512)
• Battery Capacity (mAh) The battery life is measured in milliampere-hours. The abbreviation mAh stands for milliampere-hour, a unit of electrical charge that measures a battery's capacity. It indicates how much current a battery can supply over time. A higher mAh rating means the battery can store more energy and power a device for longer.
• Camera (MP) is the smartphone's camera's megapixel rating. The higher the number of MP, the better the camera's resolution.
• 5G Support If the smartphone supports 5G connectivity, Yes. Otherwise, No.
• Operating System One of two, Android or iOS.
• Price (USD) is the current market price of the smartphone in US Dollars.

This dataset provides detailed information about various smartphones available on the e-commerce platform Flipkart as of July 2023. The dataset is in CSV format and contains the following columns:

img_link: This column contains the URL link to the image of each smartphone. It can be used to retrieve and display the corresponding image for each smartphone.

phone_name: This column contains the name or model of each smartphone. It provides a unique identifier for each device in the dataset.

avg_rating: This column represents the average rating of each smartphone on Flipkart. It indicates the overall customer satisfaction level based on user ratings. The rating scale typically ranges from 1 to 5 stars, with 5 being the highest rating.

total_rating: This column indicates the total number of people who have rated each smartphone on Flipkart. It provides an understanding of the popularity and feedback from customers who have shared their ratings.

total_reviews: This column represents the total number of reviews available for each smartphone on Flipkart. It provides insights into the level of engagement and the amount of user-generated content related to each device.

discounted_price: This column contains the discounted price of each smartphone in Indian Rupees (INR). It represents the current selling price of the device after applying any applicable discounts or promotional offers.

actual_price: This column displays the actual or original price of each smartphone in Indian Rupees (INR) before any discounts. It provides a reference point for the discounted price and helps users understand the amount of savings or price reduction available.

The dataset is valuable for conducting various analyses related to smartphones available on Flipkart. Researchers, data scientists, and analysts can use this dataset to explore trends in customer ratings, reviews, pricing, and discounts. They can also perform market research, brand comparisons, sentiment analysis, and other studies related to the smartphone industry.

Please note that this dataset is specific to Flipkart and represents the smartphone market as of July 2023. It can be used to gain insights into customer preferences, pricing strategies, and overall market dynamics in the context of Flipkart's smartphone offerings.

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.

What is the SherLock dataset?

A long-term smartphone sensor dataset with a high temporal resolution. The dataset also offers explicit labels capturing the to activity of malwares running on the devices. The dataset currently contains 10 billion data records from 30 users collected over a period of 2 years and an additional 20 users for 10 months (totaling 50 active users currently participating in the experiment).

The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. However, this dataset can be used for research in domains that are not strictly security related. For example, context aware recommender systems, event prediction, user personalization and awareness, location prediction, and more. The dataset also offers opportunities that aren't available in other datasets. For example, the dataset contains the SSID and signal strength of the connected WiFi access point (AP) which is sampled once every second, over the course of many months.

To gain full free access to the SherLock Dataset, follow these two steps:

1) Read, complete and sign the license agreement. The general restrictions are:

-The license lasts for 3 years, afterwhich the data must be deleted.

-Do not share the data with those who are not bound by the license agreement.

-Do not attempt to de-anonymize the individuals (volunteers) who have contributed the data.

-Any of your publication that benefit from the SherLock project must cite the following article: Mirsky, Yisroel, et al. "SherLock vs Moriarty: A Smartphone Dataset for Cybersecurity Research." Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. ACM, 2016.

2)Send the scanned document as a PDF to bgu.sherlock@gmail.com and provide a gmail account to share a google drive folder with.

More information can be found here, or in this publication (download link).

A 2 week data sample from a single user is provided on this Kaggle page. To access the full dataset for free, please visit our site. Note: The format of the sample dataset may differ from the full dataset.

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).

Excessive use of smartphones has been associated with a number of negative consequences for individuals. Some of these consequences relate to many symptoms of behavioral addictions. The present study aims to investigate whether participants with high levels of smartphone addiction may have difficulty with their ability to wield the self-control that is needed to restrict smartphone use compared to participants with lower levels of smartphone addiction. Specifically, we expect that people with high levels of smartphone addiction may have problems in refraining from using a smartphone. In addition, we expect people with a high level of smartphone addiction may show deficiencies in cognitive tasks such as memory, executive control, and visual and auditory attention.
An ABA design was used to analyze the effects of smartphone withdrawal. The first A refers to baseline measurements: Visual RT, Auditory RT, Go/No-Go RT and N-Back RT and Eriksen flanker RT. The B refers to 3-days of smartphone withdrawal, the second A refers to the same measurements used in the baseline. In addition, several standardized scales were administered, among them: Smartphone addiction scale-short version (SAS-SV), Fear of missing out scale (FoMOs), Procrastination scale, Psychological General Well-Being Index. One hundred and one participants took part in the study. Based on median split they were divided into two groups: high levels of smartphone addiction and low levels of smartphone addiction. Moreover, thanks to an app installed on the participant's smartphone, it was possible to measure levels of compliance with the task. Results indicate that participants with low levels of smartphone addiction show less difficulty in their ability to wield the self-control needed to withdraw smartphone use and faster reaction times on cognitive tests than participants with high levels of smartphone dependence. Moreover, the profile of participants with high levels of smartphone addiction shows higher scores on the FoMOs and Procrastination scale, and lower scores in the Psychological General Well-Being Index. The results are discussed in light of self-regulation theory.

The number of smartphone users in France was forecast to continuously increase between 2024 and 2029 by in total 3.2 million users (+5.96 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 56.89 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 Belgium and Luxembourg.

simple dataset is for beginners who have started their journey in data analytics.

dataset from a real shop repair phone technician. this dataset contains some phones problems that have been fixed and some other that not fixed during two months.

• Name : brand name + model , for example : SAMSUNG A50 , Brand : SAMSUNG, Model : A50.
• Type : this device is smartphone , pad or cell 'not smartphone'.
• Problem : 1. LCD = change broken display 2.FRP = Factory reset protection . 'this issue comes from people who factory their phone and forget their google account' 3.lock = passcode 4.off = this device turn off without indicate anything 5.flash = firmware issue 6.touch = phone touch screen 7.charge = charge issue 8.battery = change battery 9.mic = microphone 10. sound = speaker issue
• fixed YES/NO = Boolean indicate if fixed or not

-- feel free to ask any question -

vote if this dataset helpful for you, to improve it