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The purpose of this paper is to gain deeper insights into performance differences of younger and older users when performing touch gestures, as well as the influence of tablet device orientation (portrait vs landscape).

The authors performed a comparative study involving 20 younger (25-45 years) and 20 older participants (65-85 years). Each participant executed six gestures with each device orientation. Age was set as a between-subject factor. The dependent variables were task completion time and error rates (missed target rate and finger lift rate). To measure various performance characteristics, the authors implemented an application for the iPad that logged completion time and error rates of the participants when performing six gestural tasks – tap, drag, pinch, pinch-pan, rotate left and rotate right – for both device orientations.

The results show a significant effect of age on completion time and error rates. Means reveal faster completion times and lower error rates for younger users than for older users. In addition, a significant effect of device orientation on error rates could be stated. Means show higher error rates for portrait orientation than for landscape orientation. Qualitative results reveal a clear preference for landscape orientation in both age groups and a lower acceptance of rotation gestures among older participants.

In this study the authors were able to show the importance of device orientation as an influencing factor on touch interaction performance, indicating that age is not the exclusive influencing factor.

This research aims to build a system that will continuously. This paper is an extended version of SECPRE 2021 paper and presents a research on the development and validation of a behavioral biometrics continuous authentication (BBCA) system that is based on users keystroke dynamics and touch gestures on mobile devices. This paper aims to build a system that will continuously authenticate the user of a smartphone.

Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, BBCA technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), which is based on the user’s keystroke dynamics and touch gestures, using a multi-layer perceptron (MLP). Also, this paper introduces a new BB collection tool and proposes a methodology for the selection of an appropriate set of BB.

The system achieved the best results for keystroke dynamics which are 97.18% accuracy, 0.02% equal error rate, 97.2% true acceptance rate and 0.02% false acceptance rate.

This paper develops a new BB collection tool, named BioPrivacy, by which behavioral data of users on mobile devices can be collected. This paper proposes a methodology for the selection of an appropriate set of BB. This paper presents the development of a BBCA system based on MLP.

The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes.

One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities.

The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data.

The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.

The purpose of this paper is to present an approach where a novel user modeling wizard for people with motor impairments is used to gain a deeper understanding of very specific (touch-based and touchless) interaction patterns. The findings are used to set up and fill a user model which allows to automatically derive an application- and user-specific configuration for natural user interfaces.

Based on expert knowledge in the domain of software/user interfaces for people with special needs, a test-case –based user modeling tool was developed. Task-based user tests were conducted with seven users for the touch-based interaction scenario and with five users for the touchless interaction scenario. The participants are all people with different motor and/or cognitive impairments.

The paper describes the results of different test cases that were designed to model users’ touch-based and touchless interaction capabilities. To evaluate the tool’s findings, experts additionally judged the participants’ performance (their opinions were compared to the tool’s findings). The results suggest that the user modeling tool could quite well capture users’ capabilities.

The paper presents a tool that can be used to model users’ interaction capabilities. The approach aims at taking over some of the (very time-consuming) configuration tasks consultants have to do to configure software according to the needs of people with disabilities. This can lead to a wider accessibility of software, especially in the area of gesture-based user interaction.

Part of the approach has been published in the proceedings of the Interactional Conference on Advances in Mobile Computing and Multimedia 2014. Significant additions have been made since (e.g. all of the touchless interaction part of the approach and the related user study).

This paper aims to design a compact scheme of behavioural biometric-based user authentication, develop an adaptive mechanism that selects an appropriate classifier in an adaptive way and conduct a study to explore the effect of this mechanism.

As a study, the proposed adaptive mechanism was implemented using a cost-based metric, which enables mobile phones to adopt a less costly classifier in an adaptive way to build the user normal-behaviour model and detect behavioural anomalies.

The user study with 50 participants indicates that our proposed mechanism can positively affect the authentication performance by maintaining the authentication accuracy at a relatively high and stable level.

The authentication accuracy can be further improved by incorporating other appropriate classifiers (e.g. neural networks) and considering other touch-gesture-related features (e.g. the speed of a touch).

This work explores the effect of adaptive mechanism on behavioural biometric-based user authentication. The results should be of interest for software developers and security specialists in deciding whether to implement such a mechanism for enhancing authentication performance on mobile phones.

The user study with 50 participants indicates that this mechanism can positively affect the authentication performance by maintaining the authentication accuracy at a relatively high and stable level. To the best of our knowledge, our work is an early work discussing the implementation of an adaptive mechanism on a mobile phone.

Smart eyewear, such as augmented or virtual reality headset, allows the projection of virtual content through a display worn on the user’s head. This paper aims to present a mobile platform, named “CARTON”, which transforms a smartphone into smart eyewear, following a do-it-yourself (DIY) approach. This platform is composed of three main components: a blueprint to build the hardware prototype with very simple materials and regular tools; a software development kit (SDK) to help with the development of new applications (e.g. augmented reality app); and, finally, a second SDK (ControlWear) to interact with mobile applications through a Smartwatch.

User experiments were conducted, in which participants were asked to create, by themselves, the CARTON’s hardware part and perform usability tests with their own creation. A second round of experimentation was conducted to evaluate three different interaction modalities.

Qualitative user feedback and quantitative results prove that CARTON is functional and feasible to anyone, without specific skills. The results also showed that ControlWear had the most positive results, compared with the other interaction modalities, and that user interaction preference would vary depending on the task.

The authors describe a novel way to create a smart eyewear available for a wide audience around the world. By providing everything open-source and open-hardware, they intend to solve the reachability of technologies related to smart eyewear and aim to accelerate research around it.

This paper aims to evaluate the effect of multi-touch behaviours on creating Android unlock patterns (AUPs) by realising that users can perform more actions in touch-enabled mobile phones.

The author conducted two user studies with a total of 45 participates and performed two major experiments in the main user study.

The user study indicates that the multi-touch behaviours can have a positive impact on creating patterns; however, there are only nine touchable points for the original AUPs, which may reduce the usability when performing a multi-touch movement.

An even larger user study could be conducted to further analyse the patterns generated by users, that is, to analyse the specific password space by integrating the behaviours of multi-touch and to involve more types of multi-touch behaviours in creating an AUP.

This work explores the effect of multi-touch movement on creating AUPs. The results should be of interest for software developers and security researchers for exploring the effect of multi-touch behaviours on the creation of graphical passwords on mobile phones.

The author conducts two user studies with a total of 45 participants to investigate the impact of multi-touch behaviours on creating AUPs. In addition, to address the issue of usability, the author proposes two ways: increasing the number of touchable points and improve the rules of pattern creation.

The purpose of this paper is to examine the effects of interaction techniques (e.g. swiping and tapping) and the range of thumb movement on interactivity, engagement, attitude, and behavioral intention in single-handed interaction with smartphones.

A 2×2 between-participant experiment (technological features: swiping and tapping×range of thumb movement: wide and narrow) was conducted to study the effects of interaction techniques and thumb movement ranges.

The results showed that the range of thumb movement had significant effects on perceived interactivity, engagement, attitude, and behavioral intention, whereas no effects were observed for interaction techniques. A narrow range of thumb movement had more influence on the interactivity outcomes in comparison to a wide range of thumb movement.

While the subject of actual and perceived interactivity has been discussed, the issue has not been applied to smartphone. Based on the research results, the mobile industry may come up with a design strategy that balances feature- and perception-based interactivity.

This study adopted the perspective of the hybrid definition of interactivity, which includes both actual and perceived interactivity. Interactivity effect outcomes mediated by perceived interactivity.