• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5805-5825
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• 2019

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

• Smart Media Journal
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• v.1 no.4
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• pp.18-26
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• 2012

The increase of the number of smartphone applications has enforced the importance of new user interface emergence and has raised the interest of research in the convergence of multiple sensors. In this paper, we propose a method for the convergence of acceleration, magnetic and gyro sensors to recognize the gesture from motion of user smartphone. The proposed method first obtain the 3D orientation of smartphone and recognize the gesture of hand motion by using HMM(Hidden Markov Model). The proposed method for the representation for 3D orientation of smartphone in spherical coordinate was used for quantization of smartphone orientation to be more sensitive in rotation axis. The experimental result shows that the success rate of our method is 93%.

• Journal of Electrochemical Science and Technology
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• v.14 no.3
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• pp.252-261
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• 2023

Glucose detection is particularly important for clinical diagnosis and personal prevention and control. Herein, the smartphone-based amperometric glucose sensors were constructed using the NiS/CuS nanorods (NRs) as sensing electrodes. The NiS/CuS NRs were prepared through a facile hydrothermal process accompanied by the subsequent vulcanization treatment. The morphological and structural properties of NiS/CuS NRs were characterized with SEM, EDS, XRD, and XPS. Electrochemical measurements including cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy display that NiS/CuS NRs can act as highly efficient electrocatalyst for glucose detection. The NiS/CuS NRs electrodes present a wide detection range of 1-8000 µM for glucose sensing with the sensitivity of 956.38 µA·mM^-1·cm^-2. The detection limit was 0.35 µM (S/N=3). When employed in smartphone-based glucose sensing device, they also display a high sensitivity of 738.09 µA·mM^-1·cm^-2 and low detection limit of 1.67 µM. Moreover, the smartphone-based glucose sensing device also presents favorable feasibility in determination of glucose in serum samples with the recoveries ranging between 99.5 and 105.8%. The results may provide a promising viewpoint to design other new portable glucose sensors.

• Electronics and Telecommunications Trends
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• v.33 no.3
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• pp.89-99
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• 2018

Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

• Journal of Korea Game Society
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• v.12 no.6
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• pp.15-24
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• 2012

The recent growth of smartphone users and high speed network has fueled the expansion of the market for mobile application. Among these applications with various purposes, smartphone games are the hottest contents on this market. Smartphone games are far different from former games due to their new input and output interface (e.g. touch sensors and gyro sensors).It is possible that the most resonable way to categorize these new smartphone games is comparisons of different objects, in this case, various smarphone games. But there exists no classification of the games neither for the use of the market nor the purpose of researches. Even though there are few classifications for games, those classifications have a major flaw that their criteria are derived from superficial features of games. In this paper, we propose a new method to categorize smartphone games through game mechanics of principal design components.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.143-154
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• 2015

This study aims to evaluate smartphone gaming controllers. Diffusion of smartphone makes its users to play smartphone games in ease and comfort and its built-in sensors deliver new gaming experience to the users. Based on the concept how the controller system is important, the current research also implies the importance of customizing service which gives users a selection to deploy a controller. To explore the interaction effect of controllers and customizing on interactivity, flow, usability, attitude, and intention, the research constructs 3(gyroscope, wheel, and button controllers) by 2(default and customizing setting) experimental design and forty college students played Gameloft's Asphalt 8: Airborne in a within subject design. The results showed that interaction effect and customizing main effect were not found, but controller main effect was statistically significant. Button controller is superior to those other two in more detail. It implies that it is still not useful to play new types of gaming controller, and a customizing service. It suggests that smartphone games should more focus on improving optimal user experience with built-in sensor controllers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.141-147
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• 2014

With the rapid development of major functions in a smartphone, many applications interacting with a smartphone are introduced and mobile applications converged with various related technologies are announced and rapidly disseminated recently. In this paper, we propose the design of a smart mobile application operated with devices like sensors in order to utilize various functions of the smartphone effectively. The mobile application proposed in this paper uses the bluetooth module of the smartphone, composes the wireless ad hoc network with various sensors around us, and collects bionic context information of the user in real time. The smart application designed and implemented in this paper is the sensor based healthy diet application that can fulfill healthy diet program by reflecting the user's sensed bionic context data in real time.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1011-1017
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• 2012

Augmented Reality is being widely used not only for Smartphone users but also in industries such as maintenance, construction area. With smartphone, due to the low localization accuracy and the requirement of special infrastructure, current LBS (Localization Based Service) is limited to show P.O.I. (Point of Interest) nearby. Improvement of IMU (Inertial Measurement Unit) based deadreckoning is presented in this paper. Additional sensors such as the magnetic compass and magnetic flux sensors are used as well as the accelerometer and the gyro for getting more movement information. Based on the pedestrian movement, appropriate sensor information is selected and the complementary filter is used in order to enhance the accuracy of the localization. Additional sensors are used to measure the movements of the upper body and the head and to provide the user's line of sight.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.119-125
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• 2018

Various techniques for indoor positioning using a smart phone have been studied. Among them, the positioning technology using the acceleration sensor and the gyro sensor built in the smartphone is widely used in conjunction with the WiFi fingerprint technology. The location tracking technology using sensors has been used for a long time, but the performance environment of the smartphone is poor and the user is moving with the smartphone in a certain posture. Therefore, in order to improve the accuracy of location tracking in a smartphone environment, it is necessary to study and develop appropriate algorithms in a mobile environment. In this paper, we analyze the performances of frequency analysis method, maximum sum of minimum acceleration method and adaptive threshold method, which are the user's moving step count detection algorithms, and determine the most accurate method.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1200-1207
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• 2017

Sensors in a smartphone can be used to measure various physical quantities. In this paper, we propose an object localization scheme in a three dimenstional using a smart phone. The proposed scheme estimates the location of an object by observing it from several different points. The direction to the target object and the locations of the observation points are collected at each observation point using the location sensor and the orientation sensor in the smartphone. Based on these observations, the proposed scheme derives three dimensional line of sight vectors and estimates the location of the target object that minimizes the estimation error. We implemented the proposed scheme on an Android smartphone and tested its performance by estimating the height of a building and characteristics of the proposed approach.