A user study on tailoring GUIs for smartphones

Alonso-Ríos, David; Raneburger, David; Popp, Roman; Kaindl, Hermann; Falb, Jürgen

doi:10.1145/2554850.2555085

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Komine et al [ 66 ] investigated how the size of the command button affects the physical performance of a user and the psychological perception of a smartphone. Alonso et al [ 67 ] found that the graphical user interface is not specifically tailored for different smartphones with touchscreens. They conducted a user study and provided evidence that it is important to tailor the graphical user interface to make good use of the display size of a given smartphone.…”

Section: Related Workmentioning

confidence: 99%

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones

Guo

Huang

et al. 2016

Sensors

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As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.

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Section: Related Workmentioning

confidence: 99%

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones

Guo

Huang

et al. 2016

Sensors

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Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach

Mosqueira-Rey,

Hernández-Pereira,

Bobes-Bascarán

et al. 2023

Neural Comput & Applic

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Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.

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