The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user’s lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user’s sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user’s lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Chronic kidney disease (CKD) is one of the leading medical ailments in developing countries. Due to the limited healthcare infrastructure and the lack of trained human resources, the CKD problem aggravates if it is not addressed in its earlier stages. In this regard, the role of machine learning-based automated diagnosis systems plays a vital role to deal with the CKD problem. In most of the studies conducted on the automated CKD decision modeling, the main emphasis is given to enhancing the predictive accuracy of the system. In this study, we focus on the applicability challenges of automated decision systems taking CKD diagnosis as a case study within the purview of developing countries. In this regard, we propose a cost-sensitive ensemble feature ranking method that takes a more realistic approach to group-based feature selection. Two candidate solutions are proposed for group-based feature selection to meet different objectives. Subsequently, both the candidate solutions are combined into a consolidated solution. It is pertinent to note that it is one of the first studies in which cost-sensitive ensemble feature ranking for non-overlapping groups is successfully demonstrated to achieve the stated objectives i.e. low-cost and high-accuracy solution. Based on an extensive set of experiments, we demonstrate that a cost-effective and accurate solution for the CKD problem can be obtained. The experimentation includes 7 well-known classification algorithms and 8 comparative feature selection methods to show the efficacy of the proposed approach. It is concluded that the applicability of the automated CKD systems can be enhanced by including the cost consideration into the objective space of the solution formulation. Therefore, a trade-off solution can be obtained that is cost-effective and yet accurate enough to serve as a CKD screening system.