Ivan Pretel

Remote assessment of hybrid mobile applications: New approach in real environments

Inside the usability research field, mobile devices have gained a lot of importance. This kind of devices has improved exponentially. They have acquired personal computer working capabilities and small sensors that enrich their software applications. However, the development of the usability field for these applications and devices has not improved at such a fast pace. Although there are several standards and evaluation methodologies, there are no specific remote evaluation tools for dynamic environments, which are very representative of this type of device contexts. Consequently, this paper describes a remote assessment platform that records context in use properties and user interactions with the mobile device. A preliminary evaluation of the platform using a real hybrid mobile application is presented, where the interaction bias level introduced by the assessment platform is studied.

Person-oriented interface transparent access to interpersonal communication services

New communication and social interaction tools, such as social networks, are not usually designed taking into account collectives with little technological knowledge and interaction difficulties, as is the case of the elderly. This leads to an undesirable increase in the digital divide and the degree of isolation corresponding to these groups. Hence, in this work we propose the HiSozial platform, which, making use of person-oriented interfaces, service encapsulation, a combination of diverse interaction technologies and personalization, aims to make these tools accessible to the aforementioned collectives. We also present the evaluation carried out to demonstrate the validity of our proposal, including a comparative study with two applications which are not targeted at collectives with interaction difficulties.

Effectiveness Measurement Framework for Field-Based Experiments Focused on Android Devices

Most of the mobile phones have turned into full-connected devices. This provides companies with a perfect channel to interact with their potential clients and employees. The quality of the experience with these applications can directly affect the profits of the company it represents. Focusing on the mobile field and its extremely dynamic context, the quality of the experience can highly fluctuate. Inside this field, several methods and tools have been developed by defining a context of use. However, current methods can only capture it through adding external capture tools (added cameras, human observers...) that can change the experience. The main contribution in this article is a new approach to automatically measure effectiveness through a tiny but powerful mobile tool that can capture interaction metrics and the surrounding context without biasing the measured experience.

Mobile-Human Interaction Monitoring System

Information and Knowledge Society is involved into a more challenging phenomenon than ever. The ability of mobile devices to access information and services from anywhere and anytime is the main reason that empowers the massive usage of this kind of technology. Software quality has to be improved by developing mobile device interaction models according to the user necessities. These necessities have more kind of users than ever. It means the quality in use improvement is vital to the interaction. By this work we aim to give a more explicit point of view of the problems that appear during mobile application quality testing. In order to do so, we have studied a context model for mobile interaction design and the existing ways to capture, analyze and evaluate the user interaction. Finally we present one software solution consisted by a tiny mobile application and a desktop application. The exposed system can capture all necessary information to calculate quality in use metrics defined within ISO/IEC 9126 standard. The contribution revealed is a new approach to quality testing methodology focused on mobile applications where it is possible to improve the reliability of its results by paying special attention to minimize the influence of external elements used to monitor the interaction.

H2020 692819 Simpatico - D4.2: Basic Methods And Tools For Human Computation

This document is the deliverable “D4.2 – Basic methods and tools for human computation” of the European project “SIMPATICO - SIMplifying the interaction with Public Administration Through Information technology for Citizens and cOmpanies” (hereinafter also referred to as “SIMPATICO”, project reference: 692819). SIMPATICO addresses a strategic challenge towards the innovation and modernization of the public sector: the need to offer a more efficient and more effective experience to companies and citizens in their daily interaction with Public Administration (PA) by (i) offering a personalized delivery of PA online services; (ii) enabling a better comprehension of the complex processes and documents (forms, regulations, etc.) behind these services; (iii) engaging them to improve the administration processes and services. SIMPATICOs goal is to improve the experience of citizens and companies in their daily interactions with the public administration by providing a personalized delivery of e-services based on Natural Language Processing techniques and by promoting an active engagement of people for the continuous improvement of the interaction with these services. This report includes the results of project tasks T4.2 “Social question answering engine”, T4.3 “Collaborative procedures designer” and T4.4 “Citizenpedia collective knowledge base and API”. Each of these tasks corresponds to a software module of the Citizenpedia, the Question & Answer Engine (QAE), the Collaborative Procedure Designer (CPD) and the Collective Knowledge Base (CKB) respectively. This deliverable details the current implemented functionalities, the interfaces and the next steps for each the mentioned components.

Facing up Social Activity Recognition Using Smartphone Sensors

In the last years context awareness has become a reality in real-world applications. However, building comprehensive context recognition systems which are able to recognize both low and high-level context information remains a challenge. In this paper, we discuss environment recognition as a means to address the issue of recognizing a high-level user context, social activity. In many countries, bars, pubs and similar establishments are one of the main places where social engagement takes place, and thus we propose recognizing these types of environments using data collected from mobile device sensors as a proxy for inferring social activity. For this purpose, we discuss the common defining characteristics of these establishments and the sensors we will use to recognize them. After that, we introduce the design of our system. Finally, we present the preliminary evaluation carried out to assess the validity of our proposal.

The Behaviour Assessment Model for the Analysis and Evaluation of Pervasive Services

Several mobile acceptance models exist today that focus on user interface handling and usage frequency evaluation. Since mobile applications reach much deeper into everyday life, it is however important to better consider user behaviour for the service evaluation. In this paper we introduce the Behaviour Assessment Model (BAM), which is designed to gaining insights about how well services enable, enhance and replace human activities. More specifically, the basic columns of the evaluation framework concentrate on (1) service actuation in relation to the current user context, (2) the balance between service usage effort and benefit, and (3) the degree to which community knowledge can be exploited. The evaluation is guided by a process model that specifies individual steps of data capturing, aggregation, and final assessment. The BAM helps to gain stronger insights regarding characteristic usage hotspots, frequent usage patterns, and leveraging of networking effects showing more realistically the strengths and weaknesses of mobile services.

Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns

Several mobile acceptance models exist today that focus on user interface handling and usage frequency evaluation. Since mobile applications reach much deeper into everyday life, it is however important to better consider user behaviour for the service evaluation. In this paper we introduce the Behaviour Assessment Model (BAM), which is designed to gaining insights about how well services enable, enhance and replace human activities. More specifically, the basic columns of the evaluation framework concentrate on (1) service actuation in relation to the current user context, (2) the balance between service usage effort and benefit, and (3) the degree to which community knowledge can be exploited. The evaluation is guided by a process model that specifies individual steps of data capturing, aggregation, and final assessment. The BAM helps to gain stronger insights regarding characteristic usage hotspots, frequent usage patterns, and leveraging of networking effects showing more realistically the strengths and weaknesses of mobile services.