Wearable devices: new ways to manage information

Billinghurst, Mark; Starner﻿﻿, Thad

doi:10.1109/2.738305

Cited by 197 publications

(97 citation statements)

References 6 publications

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“…여 기에 웨어러블 컴퓨터의 이동성을 위해 초소형, 저전 력화 기술, 플렉서블 기술, 인터렉션 기술, 웨어러블 통신 기술, 신소재 기술, 센서 기술 등이 필요하다 [2][3][4] . 자세히 설명되어 있지 않았다 [5] . 성들에 대한 명확한 메트릭의 제시가 미흡하다 [7] .…”

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Quality Assessment Model for Practical Wearable Computers

Oh¹,

Choi²,

Kim³

et al. 2014

The Journal of Korean Institute of Communications and Informati

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Recently, the progress of smart phone market has retarded by oversupply therefore wearable computer has been the focus of new growth engine. Wearable computing system is a complex fusion of a variety of technologies such as wireless network, embedded, sensor and new material. Because these technologies involves utilization and mobility in addition to quality characteristic in existing software, application of ISO/IEC 9126 is not perfect when assessing quality of wearable computer. In this study, author suggested new quality assessment model for wearable computer by sorting quality attribute in ISO/IEC 9126 and adding new quality attribute. For this, author investigated features and functional requirements related to wearable computer. and then author suggested quality standard and metrics by identifying quality characteristic. Author confirmed practicality of quality assessment model by using suggested model in scenario and comparing quality assessment of three goods such as company S, L, G. This quality assessment model is expected to use guidelines for assessing quality of wearable computer.

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Quality Assessment Model for Practical Wearable Computers

Oh¹,

Choi²,

Kim³

et al. 2014

The Journal of Korean Institute of Communications and Informati

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“…[1]) that are equipped with various sensors to support versatile compute-intensive tasks in the field of image processing, multimedia, wearable computing, augmented reality, mobile healthcare etc. A recent study shows the number of mobile application downloads all over the world in 2016 was 149.3 billion which is expected to be 352.9 billion in 2021.…”

Section: Introductionmentioning

confidence: 99%

Effective task migration to reduce execution time in mobile cloud computing

Saha

Hasan

2017

2017 23rd International Conference on Automation and Computing (ICAC)

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Abstract-With the advancements of mobile technologies, different compute-intensive tasks are emerging rapidly. However, due to resource constraints, these tasks are facing challenges to execute on mobile devices. As a solution to this problem, cloud migration has been introduced to execute a task on the cloud and then to return the results to the user mobile device. In this paper, a cloud migration decision making algorithm for compute-intensive tasks has been proposed to determine the feasibility of execution on a cloud server instead of a mobile device. Furthermore, the performances between mobile and cloud executions have been investigated which shows that the task completion time can be minimised by 6-8 times when a cloud server is utilised.

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“…The wireless sensor network technology has been used in industrial systems and smart homes [1][2][3]. Several wearable devices that can collect a large amount of physical activity data from sensors attached to a human body have been developed [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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Wearable devices: new ways to manage information

Cited by 197 publications

References 6 publications

Quality Assessment Model for Practical Wearable Computers

Quality Assessment Model for Practical Wearable Computers

Effective task migration to reduce execution time in mobile cloud computing

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

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