Gut microbiome research has made tremendous progress, especially with the integration of machine learning and artificial intelligence that can provide new insights from complex microbiome data and its impact on human health. The use of explainable artificial intelligence is becoming critical in medicine and adopting it in precision medicine-models leveraging gut microbiome data is appealing for providing more transparency and trustworthiness in clinical research. This scoping review evaluates the use of machine learning and explainable artificial intelligence techniques and identifies existing gaps in knowledge in this research area to suggest future research directions. Online databases (PubMed and Scopus) were searched to retrieve papers published between 2018-2024, and from which we selected 76 publications. Different clinical applications of machine learning and artificial intelligence techniques in gut microbiome studies were explored in the reviewed articles. We observed a high prevalence in the use of black box models in the field, with Random Forest being the most used algorithm. The explainability remains somewhat limited in the field, but it appears to be improving. Researchers showed interest in SHAP applications as an explainable technique. Finally, not enough attention was paid to the reproducibility of the research work published. This review highlights opportunities for advancing research on explainable artificial intelligence models in the field of microbiome, supporting future applications of microbiome-based precision medicine.

Synthetic data generation is an emerging solution to address current data privacy and availability challenges, especially in healthcare applications and wearable devices. This study aims to generate synthetic multivariate time-series data that simulates physical activities, preserving the variability and dependencies observed in real-world data (RWD). An innovative approach that includes data organization, preprocessing, and model training on a dataset of 100 users with several datetime, numeric, and categorical variables is conducted. By using two artificial intelligence (AI) models — Periodic Autoregressive (PAR) and Conditional Tabular Generative Adversarial Network (CTGAN)—a total of 100 new synthetic users with temporal and feature-specific activity patterns were generated. Evaluation results showed strong alignment between synthetic and original data, with mean values of 0.014 for Kolmogorov-Smirnov (KS) Test, 0.057 for Jensen-Shannon (JS) Distance, and 0.011 for Pairwise Correlation, indicating realistic data relationships and feature distributions. Despite these preliminary results, future work includes enhancing computational efficiency and scalability, expanding its generalizability across diverse datasets, and further validation.

Abstract With the new Industry 5.0 future factories can effectively face the aged workforce challenge, making workflows more enriched and flexible and capable to increase work well-being. This paper described how self-quantified worker could be a successful tool to achieve with this with a careful collaborative design. Our vision aims at empowering the aged workers and engage them with the work community based on adapting the factory shop floor routines to their changing needs while they age and support the aged worker to understand and develop his/her own competence.

Abstract The presented platform architecture and deployed implementation in real-life clinical and home care settings on four Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) study sites, integrates the novel working tools for improved disease management with the initial releases of the AI models for disease monitoring. The described robust industry-standard scalable platform is to be a referent example of the integration approach based on loose coupling APIs and industry open standard human-readable and language-independent interface specifications, and its successful baseline implementation for further upcoming releases of additional and more advanced AI models and supporting pipelines (such as for ALS and MS progression prediction, patient stratification, and ambiental exposure modelling) in the following development.

Abstract The presented demonstrated working tools in the initial version constitute the foundation of the novel ALS and MS management and monitoring, leveraging extended IoT sensing and emerging instruments infrastructure, and a basis for integration of more advanced and effective AI models (in development) for disease progression prediction, patient stratification and ambiental exposure assessment.

The paper presents the Pulse project, a European project founded under the Horizon 2020 program.

Mild Cognitive Impairment (MICI) symptoms are one of the main issues that contribute, in older people, to the difficulty to live independently, social isolation and loss of autonomy. INFINITy solution provides a set of services aimed to reinforce and support the daily routines of people with MCI for both indoor and outdoor scenarios. A co-design session with end-users were performed in order to better adapt the INFINITy solution to the needs and characteristics of the target beneficiaries. Results show the feedback received form end-users regarding different aspects of the solution such as: functionalities, use cases, and interfaces. The results were useful to improve the INFINITy solution to better address user’s needs and preferences.

Air pollution in one of the main problems that big cities have nowadays. Traffic congestion, heaters, industrial activities, among others produce large quantities of Particulate Matter (PM) that have harmful effects on citizens’ health. This paper presents the design, development and initial validation of a wearable device for the detection of PM concentration, with communication capacity via WiFi and Bluetooth Low Energy and an end user interface. The results are promising due to the high accuracy of measurements collected by the developed device. This solution is a step forward in empowering citizens to prevent being exposed to high levels of air pollution and is the beginning of what could be a macro-network of air quality sensors within a Smart City.

Existing initiatives to improve physical, mental, and social condition of senior citizens, which in Europe fall under the name of Active and Healthy Ageing, are including technological paradigms as main driver for innovation uptake. Among these paradigms, Smart Cities and the Internet of Things are of utmost importance. However, these initiatives may benefit from unified visions, efforts, and frameworks when it comes to defining technological solutions that take the most of both paradigms. We have defined an iterative approach, which combines user centred design techniques, technological development approaches, and a multifaceted adaptation process, to define a solution for Active and Healthy Ageing that makes use of the two paradigms. The solution is being defined in the context of two research and innovation projects, City4Age and ACTIVAGE, during which a solution is going to be defined and evaluated in the city of Madrid. Results show how Smart Cities and Internet of Things contribute to the solution, from a user (user needs and use cases) and a service delivery (technologies, architecture, and suppliers) perspective. In conclusion, we find the cocreation framework extremely useful for the Active and Health Ageing domain, and the proposed implementation of it is functioning, although there is room for improvement.