In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors.

Background: Fecal microbiota transplantation (FMT) from humans with specific medical conditions to animal models can demonstrate causality by inducing or exacerbating pathophenotypes, linking the gut microbiota to health outcomes. Methods: We conducted a scoping review searching MEDLINE, EMBASE, Scopus, and Web of Science through July 2024 to identify human noninfectious diseases studied using FMT in animal models, investigate FMT methodologies, and assess the feasibility of systematic reviews on the role of the microbiota in specific diseases. Results: From 605 reports of 489 studies, we found that inflammatory bowel diseases, irritable bowel syndrome, obesity, colorectal cancer, and depression were the most commonly studied, with cancer research focusing on immunotherapy non-responsiveness. In a random sample of studies, gastrointestinal outcomes were most frequently reported, with remarkably high rates (> 80%) of successful induction of disease-specific alterations for intestinal barrier function, gastrointestinal inflammation, circulating immune parameters, and fecal metabolites. Most studies used C57BL/6 mice and oral gavage administration, with recipients being either germ-free or antibiotic-pretreated. We created tables linking conditions with publications to facilitate future systematic reviews. Conclusions: Although human-to-animal FMT studies cover diverse conditions, methodological heterogeneity and inconsistent reporting hinder comparability. Standardized protocols and guidelines are needed. For several conditions, sufficient literature exists to assess the role of the gut microbiota in human health through systematic reviews.

In recent years, blockchains systems have seen massive adoption in retail and enterprise environments. Cryptocurrencies become more widely adopted, and many online businesses have decided to add the most popular ones, like Bitcoin or Ethereum, next to Visa or Mastercard payments. Due to the decentralized nature of blockchain-based systems, there is no possible way to revert confirmed transactions. It may result in losses caused by human error or poor design of the user interface. We created a cryptocurrency wallet with a full on-chain solution for aliasing accounts and tokens to improve user experience and avoid unnecessary errors. The aliasing system consists of a number of smart contracts deployed on top of the blockchain network that give the ability to register aliases to accounts and tokens and use them instead of opaque addresses. Our solution shows how performant modern blockchains are and presents a way of building fully decentralized applications that can compete with centralized ones in terms of performance.

As technology becomes more ubiquitous and pervasive, special attention should be given to human-computer interaction, especially to the aspect related to the emotional states of the user. However, this approach assumes very specif c mode of data collection and storage. This data is used in the affective computing experiments for human emotion recognition. In the paper we describe a new software solution for mobile devices that allows for data acquisition from wristbands. The application reads physiological signals from wristbands and supports multiple recent devices. In our work we focus on the Heart Rate (HR) and Galvanic Skin Response (GSR) readings. The recorded data is conveniently stored in CSV files, ready for further interpretation. We provide the evaluation of our application with several experiments. The results indicate that the BandReader is a reliable software for data acquisition in affective computing scenarios.

Background: An increasing number of online support groups are providing advice and information on topics related to mental health. Objective: This study aimed to investigate the needs that internet users meet through peer-to-peer interactions. Methods: A search of 4 databases was performed until August 15, 2022. Qualitative or mixed methods (ie, qualitative and quantitative) studies investigating interactions among internet users with mental disorders were included. The φ coefficient was used and machine learning techniques were applied to investigate the associations between the type of mental disorders and web-based interactions linked to seeking help or support. Results: Of the 13,098 identified records, 44 studies (analyzed in 54 study-disorder pairs) that assessed 82,091 users and 293,103 posts were included. The most frequent interactions were noted for people with eating disorders (14/54, 26%), depression (12/54, 22%), and psychoactive substance use disorders (9/54, 17%). We grouped interactions between users into 42 codes, with the empathy or compassion code being the most common (41/54, 76%). The most frequently coexisting codes were request for information and network (35 times; φ=0.5; P<.001). The algorithms that provided the best accuracy in classifying disorders by interactions were decision trees (44/54, 81%) and logistic regression (40/54, 74%). The included studies were of moderate quality. Conclusions: People with mental disorders mostly use the internet to seek support, find answers to their questions, and chat. The results of this analysis should be interpreted as a proof of concept. More data on web-based interactions among these people might help apply machine learning methods to develop a tool that might facilitate screening or even support mental health assessment.

Aim: To assess the effectiveness of perioperative psychological interventions provided to patients with clinically severe obesity undergoing bariatric surgery regarding weight loss, BMI, quality of life, and psychosocial health using the Bayesian approach. Methods: We considered randomised trials that assessed the beneficial and harmful effects of perioperative psychological interventions in people with clinically severe obesity undergoing bariatric surgery. We searched four data sources from inception to 3 October 2021. The authors independently selected studies for inclusion, extracted data, and assessed the risk of bias. We conducted a meta-analysis using a Bayesian approach. PROSPERO: CRD42017077724. Results: Of 13,355 identified records, we included nine studies (published in 27 papers with 1060 participants (365 males; 693 females, 2 people with missing data)). Perioperative psychological interventions may provide little or no benefit for BMI (the last reported follow-up: MD [95% credible intervals] = −0.58 [−1.32, 0.15]; BF₀₁ = 0.65; 7 studies; very low certainty of evidence) and weight loss (the last reported follow-up: MD = −0.50 [−2.21, 0.77]; BF₀₁ = 1.24, 9 studies, very low certainty of evidence). Regarding psychosocial outcomes, the direction of the effect was mainly inconsistent, and the certainty of the evidence was low to very low. Conclusions: Evidence is anecdotal according to Bayesian factors and uncertain whether perioperative psychological interventions may affect weight-related and psychosocial outcomes in people with clinically severe obesity undergoing bariatric surgery. As the results are ambiguous, we suggest conducting more high-quality studies in the field to estimate the true effect, its direction, and improve confidence in the body of evidence.