Personalisation is key to creating successful digital health applications. Recent evidence links personality and preference for digital experience — suggesting that psychometric traits can be a promising basis for personalisation of digital mental health services. However, there is still little quantitative evidence from actual app usage. In this study, we explore how different personality types engage with different intervention content in a commercial mental health application. Specifically, we collected the Big Five personality traits alongside the app usage data of 126 participants using a mobile mental health app for seven days. We found that personality traits significantly correlate with the engagement and user ratings of different intervention content. These findings open a promising research avenue that can inform the personalised delivery of digital mental health content and the creation of recommender systems, ultimately improving the effectiveness of mental health interventions.

In this paper, we present the Algorithmic Audit (AA) of REM!X, a personalized well-being recommendation app developed by Telefónica Innovación Alpha. The main goal of the AA was to identify and mitigate algorithmic biases in the recommendation system that could lead to the discrimination of protected groups. The audit was conducted through a qualitative methodology that included five focus groups with developers and a digital ethnography relying on users comments reported in the Google Play Store. To minimize the collection of personal information, as required by best practice and the GDPR [1], the REM!X app did not collect gender, age, race, religion, or other protected attributes from its users. This limited the algorithmic assessment and the ability to control for different algorithmic biases. Indirect evidence was thus used as a partial mitigation for the lack of data on protected attributes, and allowed the AA to identify four domains where bias and discrimination were still possible, even without direct personal identifiers. Our analysis provides important insights into how general data ethics principles such as data minimization, fairness, non-discrimination and transparency can be operationalized via algorithmic auditing, their potential and limitations, and how the collaboration between developers and algorithmic auditors can lead to better technologies

Recommender Systems have not been explored to a great extent for improving health and subjective wellbeing. Recent advances in mobile technologies and user modelling present the opportunity for delivering such systems, however the key issue is understanding the drivers of subjective wellbeing at an individual level. In this paper we propose a novel approach for deriving personalized activity recommendations to improve subjective wellbeing by maximizing the congruence between activities and personality traits. To evaluate the model, we leveraged a rich dataset collected in a smartphone study, which contains three weeks of daily activity probes, the Big-Five personality questionnaire and subjective wellbeing surveys. We show that the model correctly infers a range of activities that are 'good' or 'bad' (i.e. that are positively or negatively related to subjective wellbeing) for a given user and that the derived recommendations greatly match outcomes in the real-world.

The exponential growth in smartphone adoption is contributing to the availability of vast amounts of human behavioral data. This data enables the development of increasingly accurate data-driven user models that facilitate the delivery of personalized services that are often free in exchange for the use of its customers’ data. Although such usage conventions have raised many privacy concerns, the increasing value of personal data is motivating diverse entities to aggressively collect and exploit the data. In this article, we unfold profiling scenarios around mobile HTTP(S) traffic, focusing on those that have limited but meaningful segments of the data. The capability of the scenarios to profile personal information is examined with real user data, collected in the wild from 61 mobile phone users for a minimum of 30 days. Our study attempts to model heterogeneous user traits and interests, including personality, boredom proneness, demographics, and shopping interests. Based on our modeling results, we discuss various implications to personalization, privacy, and personal data rights.

In studies so far have been shown that clinical analysis of the gait parameters is extremely important for improving diagnostics and following illness or deficiencies of lower extremities, as well as for analyzing recuperation levels after therapy. Purpose of this paper is to analyze possibility of using easily affordable web cameras for recording knee movement. Recordings are made with web cam on Fujitsu lifebook E laptop. Algorithm for object tracking is made in programming environment MatLAB. Five participants have participated in this research. Results are graphically presented for left and right leg for one gait cycle along straight line and values for anterior posterior (AP) translation are shown in tables for every phase of gait cycle. Acquired study results show that it is possible to measure observed parameter in this way and that values correspond with values obtained in previous studies.

Mobile technology is becoming a loyal companion in our lives. It is used for increasing amounts of time during the day and night, enabling the development of intelligent user interfaces that characterize their users' traits and adapt to them. In this paper, we show how an individual's tendency to experience boredom, i.e. the personal trait called boredom proneness, affects the use of technology -- specifically a smartphone. We develop machine learning models to automatically classify individuals into high/low boredom proneness from their typical daily patterns of smartphone use. We thus propose boredom proneness as a trait with high potential to enable the design of personalized mobile services that are more meaningful to their users.

Situated displays can support behavior management for children with behavioral challenges. However, existing tools are often static, rarely engaging, and tend to focus only on individual behavior. In this work, we designed and deployed a situated display to support teamwork and cooperation in children with behavioral challenges. We evaluated this tool in two classrooms of a public school specializing in behavioral interventions with 28 children over four weeks. The results of this work demonstrate that situated displays focused on collective behavioral performance can support reflection on individual performance, improve behavior for students with behavioral challenges, as well as encourage teamwork and cooperative behavior in classrooms. These results also indicate a variety of issues to be considered when designing situated displays for these environments, including considerations for the representation of ambiguity and failure as well as the relationship between novelty and engagement.

Position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi-Fi signal strength fingerprinting techniques are widely used in office buildings with existing Wi-Fi infrastructure. Our previous work has proposed a solution based on exploitation of FM signal to deal with environments not covered with Wi-Fi signal or environments with only single Wi-Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibration of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi-Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. Finally, we address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system.