Survey on Smartphone Sensors and User Intent in Smartphone Usage

Bhatele, Priyanka; Bedekar, Mangesh

doi:10.1109/i2ct57861.2023.10126192

Cited by 2 publications

(12 citation statements)

References 39 publications

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“…Screen Capture, the activity done in case the content is to be further referred by the user. All these gestures are the indicators of user reading activity and the positive indicators of user interests in the content [28].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…If a user reads online content with interest, then the user tends to perform one or more of the activities as illustrated in Fig. 1 [28].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…1. User Gesture Interactions while Reading using Smartphones [28] Smartphone Sensors generate sensor data readings without any explicit permission from the user. Readings of the screen activity usually follows a pattern This paper proposes a model to implement machine learning classifiers to identify these activities using smartphone sensors readings as the features.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…It's one of the foremost tools for pedagogical learning [21]. It acts as a common tool for referring study materials, capturing images of lecture slides and notes [20,28]. Logging academic work portals is another common usage of smartphones among students [22].…”

Section: Introductionmentioning

confidence: 99%

“…Implicit feedback methods for online reading are relatively un-explored for users using smartphones and tablets. When a user intends to read online, using the smartphone, the user will make some screen handling gestures [28] on the mobile screen. This research works proposes to capture, identify and categorize these screen activities and mobile gestures interactions using machine learning classifier algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

Preprint

Self Cite

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In the recent times, smartphone usage has become increasingly popular for learning. User’s exhibit multiple gesture interactions with smartphones, while reading, which can provide valuable implicit feedback about the content consumed. Smartphones have many embedded sensors which capture plethora of user interaction data. The on-device Gyroscope and Accelerometer can be enabled to capture the variations done due to gesture interactions like scrolling, pinch to zoom, tap, orientation change and screen capture. This research work is based on training machine learning classifier models with smartphone sensors’ readings to identify the users screen gesture interactions. Data for the classifier is collected by from 47 users in total using an android application. Aggregated time domain feature extraction has been computed on the preprocessed data. Four groups of data have been used to train the models. Extensive experiments are done to test the success of proposed system using Random Forests, Support Vector Machine (SVM), Extreme Gradient Boost (XGB), ADA boost, Naïve Bayes (NB) and K-Nearest Neighbour (KNN). Detailed analysis of the success rate and accuracy calculation have been performed. Best identification accuracy of 97.58% is achieved by Random Forest Classifier followed by Extreme Gradient Boost and K-Nearest Neighbour with accuracy 95.97% and 93.55% respectively.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

“…If a user reads online content with interest, then the user tends to perform one or more of the activities as illustrated in Fig. 1 [28].…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

IJCDS

View full text Add to dashboard Cite

In the recent times, smartphone usage has become increasingly popular for learning. User's exhibit multiple gesture interactions with smartphones, while reading, which can provide valuable implicit feedback about the content consumed. Smartphones have many embedded sensors which capture plethora of user interaction data. The on-device Gyroscope and Accelerometer can be enabled to capture the variations done due to gesture interactions like scrolling, pinch to zoom, tap, orientation change and screen capture. This research work is based on training machine learning classifier models with smartphone sensors' readings to identify the users screen gesture interactions. 47 users performed various screen gestures using an android application and contributed in data collection activity. Aggregated time domain feature extraction has been computed on the preprocessed data. Four groups of data have been used to train the models. Extensive experiments are done to test the success of proposed system using Random Forests (RFC), Support Vector Machine (SVM), Extreme Gradient Boost (XGB), Adaptive Boost (ADA Boost), Naïve Bayes (NB) and K-Nearest Neighbour (KNN). Detailed analysis of the success rate and accuracy calculation have been performed. Best identification accuracy of 97.58% is achieved by Random Forest Classifier followed by Extreme Gradient Boost (XGB) and K-Nearest Neighbour (KNN) with accuracy 95.97% and 93.55% respectively.

show abstract

Survey on Smartphone Sensors and User Intent in Smartphone Usage

Cited by 2 publications

References 39 publications

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope

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