A survey on smartphone-based systems for opportunistic user context recognition

Hoseinitabatabaei, Seyed Amir; Gluhak, Alexander; Tafazolli, Rahim

doi:10.1145/2480741.2480744

Cited by 99 publications

(59 citation statements)

References 145 publications

(196 reference statements)

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“…For instance, HoseiniTabatabaei et al [30] surveyed smartphone-based systems for opportunistic (nonintrusive) user context recognition. Their article provided introduction to typical architecture of mobile-centric user context recognition, the main techniques of context recognition, lesson learned from previous approaches, and challenges for future research.…”

Section: Previous Reviewsmentioning

confidence: 99%

Usability Studies on Mobile User Interface Design Patterns: A Systematic Literature Review

Punchoojit

Hongwarittorrn

2017

Advances in Human-Computer Interaction

128

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Mobile platforms have called for attention from HCI practitioners, and, ever since 2007, touchscreens have completely changed mobile user interface and interaction design. Some notable differences between mobile devices and desktops include the lack of tactile feedback, ubiquity, limited screen size, small virtual keys, and high demand of visual attention. These differences have caused unprecedented challenges to users. Most of the mobile user interface designs are based on desktop paradigm, but the desktop designs do not fully fit the mobile context. Although mobile devices are becoming an indispensable part of daily lives, true standards for mobile UI design patterns do not exist. This article provides a systematic literature review of the existing studies on mobile UI design patterns. The first objective is to give an overview of recent studies on the mobile designs. The second objective is to provide an analysis on what topics or areas have insufficient information and what factors are concentrated upon. This article will benefit the HCI community in seeing an overview of present works, to shape the future research directions.

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Section: Previous Reviewsmentioning

confidence: 99%

Usability Studies on Mobile User Interface Design Patterns: A Systematic Literature Review

Punchoojit

Hongwarittorrn

2017

Advances in Human-Computer Interaction

128

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“…Mannini & Sabitini [13] experimented by using five tri-axial accelerometers and learning methods were used to recognize the postures and movements of the users. Hoseini-tabatabaei et al [14] based on the research work on the human activity recognition has summarized the related research data and the approaches used in context awareness in the smartphones. As the research work continued, suggestions were made to perform the similarity search between the patterns to identify the activities.…”

Section: Related Workmentioning

confidence: 99%

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Liu¹,

Zhou²,

Shakya³

et al. 2018

IJMLC

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Abstract-Smartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs.

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“…As presented in [16], each embedded sensor of a smart electronic device (e.g. smartphones), can be categorized as inertial, positioning, or ambient.…”

Section: Diverse Resource Typesmentioning

confidence: 99%

A Service Based Architecture for Multidisciplinary IoT Experiments with Crowdsourced Resources

Alexandrou

Angelopoulos

Evangelatos

et al. 2016

Ad-Hoc, Mobile, and Wireless Networks

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Abstract. Research on emerging networking paradigms, such as Mobile Crowdsensing Systems, requires new types of experiments to be conducted and an increasing spectrum of devices to be supported by experimenting facilities. In this work, we present a service based architecture for IoT testbeds which (a) exposes the operations of a testbed as services by following the Testbed as a Service (TBaaS) paradigm; (b) enables diverse facilities to be federated in a scalable and standardized way and (c) enables the seamless integration of crowdsourced resources (e.g. smartphones and wearables) and their abstraction as regular IoT resources. The architecture enables an experimenter to access a diverse set of resources and orchestrate experiments via a common interface by hiding the underlying heterogeneity and complexity. This way, the field of IoT experimentation with real resources is further promoted and broadened to also address researchers from other fields and disciplines.

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A survey on smartphone-based systems for opportunistic user context recognition

Cited by 99 publications

References 145 publications

Usability Studies on Mobile User Interface Design Patterns: A Systematic Literature Review

Usability Studies on Mobile User Interface Design Patterns: A Systematic Literature Review

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

A Service Based Architecture for Multidisciplinary IoT Experiments with Crowdsourced Resources

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