Multi-device Use Research Articles

Patterns of multi-device use with the smartphone. A video-ethnographic study of young adults’ multi-device use with smartphones in naturally occurring contexts

Using multiple devices at the same time is becoming increasingly common in the daily lives of users, be it for work or for leisure. This paper presents in situ qualitative and quantitative evidence of multi-device use from a dataset of over 200h of first-person and interview recordings (n = 41). We discuss three different ‘patterns’ of multi device use (work, leisure, mixed use) and illustrate the user experience in detail with three participant journeys. We find that the smartphone was always ‘in the mix’; we did not observe multi-device use without the smartphone, or isolated use of other devices. Overall, we suggest that looking at transitions between activities users engage in rather than devices they use is more effective to understand multi-device use. Based on this analysis, we highlight issues around the patterns and experiences of multi-device use in everyday life and provide recommendations for design and further research.

Multi-Device Use: Understanding the Motivations behind Switching between Multiple Devices during a Task

ABSTRACT Multi-device use has become a popular pattern of device use in recent years. However, empirical studies pertaining to motivations behind intention to use multiple devices have been limited. The objective of this study focuses on answering an essential question: given that heterogeneous devices are available to users, why do users use multiple devices rather than a single device to complete a task? Both Task-Technology Fit Theory and mental workload paradigm are considered to develop eight hypotheses and build the research framework. A trip-planning scenario was designed and used in the questionnaire to understand the factors that motivate users to use multiple devices. A total of 217 subjects completed the questionnaire. The results show that perceived task fit with multi-device use forms users’ attitudes toward and expected satisfaction with multi-device use. Furthermore, users’ attitudes toward multi-device use trigger their intentions to use multiple devices. Additionally, perceived complexity of multi-device use hinders users from perceiving good task fit with multi-device use. Implications for theory, practice, and future research directions are discussed.

Density-based parallel skin lesion border detection with webCL

BackgroundDermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate borders through a hand drawn representation based upon visual inspection. Due to the subjective nature of this technique, intra- and inter-observer variations are common. Because of this, the automated assessment of lesion borders in dermoscopic images has become an important area of study.MethodsFast density based skin lesion border detection method has been implemented in parallel with a new parallel technology called WebCL. WebCL utilizes client side computing capabilities to use available hardware resources such as multi cores and GPUs. Developed WebCL-parallel density based skin lesion border detection method runs efficiently from internet browsers.ResultsPrevious research indicates that one of the highest accuracy rates can be achieved using density based clustering techniques for skin lesion border detection. While these algorithms do have unfavorable time complexities, this effect could be mitigated when implemented in parallel. In this study, density based clustering technique for skin lesion border detection is parallelized and redesigned to run very efficiently on the heterogeneous platforms (e.g. tablets, SmartPhones, multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units) by transforming the technique into a series of independent concurrent operations. Heterogeneous computing is adopted to support accessibility, portability and multi-device use in the clinical settings. For this, we used WebCL, an emerging technology that enables a HTML5 Web browser to execute code in parallel for heterogeneous platforms. We depicted WebCL and our parallel algorithm design. In addition, we tested parallel code on 100 dermoscopy images and showed the execution speedups with respect to the serial version. Results indicate that parallel (WebCL) version and serial version of density based lesion border detection methods generate the same accuracy rates for 100 dermoscopy images, in which mean of border error is 6.94%, mean of recall is 76.66%, and mean of precision is 99.29% respectively. Moreover, WebCL version's speedup factor for 100 dermoscopy images' lesion border detection averages around ~491.2.ConclusionsWhen large amount of high resolution dermoscopy images considered in a usual clinical setting along with the critical importance of early detection and diagnosis of melanoma before metastasis, the importance of fast processing dermoscopy images become obvious. In this paper, we introduce WebCL and the use of it for biomedical image processing applications. WebCL is a javascript binding of OpenCL, which takes advantage of GPU computing from a web browser. Therefore, WebCL parallel version of density based skin lesion border detection introduced in this study can supplement expert dermatologist, and aid them in early diagnosis of skin lesions. While WebCL is currently an emerging technology, a full adoption of WebCL into the HTML5 standard would allow for this implementation to run on a very large set of hardware and software systems. WebCL takes full advantage of parallel computational resources including multi-cores and GPUs on a local machine, and allows for compiled code to run directly from the Web Browser.