AI, Volume 6, Issue 6 (June 2025) – 24 articles

The individual marathon optimal pacing sparring the runner to hit the “wall” after 2 h of running remain unclear. In the current study we examined to what extent Deep neural Network contributes to identify the individual optimal pacing training a Variational Auto Encoder (VAE) with a small dataset of nine runners. This last one has been constructed from an original one that contains the values of multiple physiological variables for 10 different runners during a marathon. We plot the Lyapunov exponent/Time graph on these variables for each runner showing that the marathon wall could be anticipated. The pacing strategy that this innovative technique sheds light on is to predict and delay the moment when the runner empties his reserves and ’hits the wall’ while considering the individual physical capabilities of each athlete. Our data suggest that given that a further increase of marathon runner using a cardio-GPS could benefit of their pacing run for optimizing their performance if AI would be used for learning how to self-pace his marathon race for avoiding hitting the wall. Full article

We proposed a GAN-based method to generate a ship hull form. Unlike mathematical hull forms that require geometrical parameters to generate ship hull forms, the proposed method requires desirable ship performance parameters, i.e., the drag coefficient and tonnage. The objective of this study is to demonstrate the feasibility of generating hull geometries directly from performance specifications, without relying on explicit geometrical inputs. To achieve this, we implemented a conditional Wasserstein GAN with gradient penalty (cWGAN-GP) framework. The generator learns to synthesize hull geometries conditioned on target performance values, while the discriminator is trained to distinguish real hull forms from generated ones. The GAN model was trained using a ship hull form dataset generated using the generalized Wigley hull form. The proposed method was evaluated through numerical experiments and successfully generated ship data with small errors: less than 0.08 in mean average percentage error. Full article

This paper presents the key results of a large-scale empirical study on the adoption of Generative AI (GenAI) by the Joint Research Centre (JRC), the European Commission’s science-for-policy department. Since spring 2023, the JRC has developed and deployed GPT@JRC, a platform providing safe and compliant access to state-of-the-art Large Language Models for over 10,000 knowledge workers. While the literature highlighting the potential of GenAI to enhance productivity for knowledge-intensive tasks is abundant, there is a scarcity of empirical evidence on impactful use case types and success factors. This study addresses this gap and proposes the JRC GenAI Compass conceptual framework based on the lessons learnt from the JRC’s GenAI adoption journey. It includes the concept of AI-IQ, which reflects the complexity of a given GenAI system. This paper thus draws on a case study of enterprise-scale AI implementation in European public institutions to provide approaches to harness GenAI’s potential while mitigating the risks. Full article

This study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. The framework resolves critical limitations of conventional experience-driven approaches that inadequately address the nonlinear coupling between the spatial heterogeneity of rock mass parameters and mechanical system responses. Three principal innovations are introduced: (1) a hardware-compatible sparse attention architecture achieving O(n) computational complexity while preserving high-fidelity geological feature extraction capabilities; (2) an adaptive kernel function optimization mechanism that reduces confidence interval width by 41.3% through synergistic integration of boundary likelihood-driven kernel selection with Chebyshev inequality-based posterior estimation; and (3) a physics-enhanced modelling methodology combining non-Hertzian contact mechanics with eddy field evolution equations. Validation experiments employing field data from the Pujiang Town Plot 125-2 Tunnel Project demonstrated superior performance metrics, including 92.4% ± 1.8% warning accuracy for fractured zones, ≤28 ms optimization response time, and ≤4.7% relative error in energy dissipation analysis. Comparative analysis revealed a 32.7% reduction in root mean square error (p < 0.01) and 4.8-fold inference speed acceleration relative to conventional methods, establishing a novel data–physics fusion paradigm for TBM control with substantial implications for intelligent tunnelling in complex geological formations. Full article

This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance. Full article

Deep learning has brought substantial progress to medical imaging, which has resulted in continuous improvements in diagnostic procedures. Through deep learning architecture implementations, radiology professionals achieve automated pathological condition detection, segmentation, and classification with improved accuracy. The research tackles a rarely studied clinical medical imaging issue that involves bullet identification and positioning within X-ray images. The purpose is to construct a sturdy deep learning system that will identify and classify ballistic trauma in images. Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. Research data was developed by replicating controlled bullet damage in chest X-rays while expanding to a wider range of anatomical areas that include the legs, abdomen, and head. Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. Multiple computational systems were used to verify the results, which showcased the effectiveness of the proposed solution. This research provides new perspectives on understanding forensic radiology trauma assessment by developing the first deep learning system that detects and classifies gun-related radiographic injuries automatically. The first system for forensic radiology designed with automated deep learning to classify gunshot wounds in radiographs is introduced by this research. This approach offers new ways to look at trauma which is helpful for work in clinics as well as in law enforcement. Full article

Recent advancements in machine learning (ML) have led to state-of-the-art performance in various domain-specific tasks, driving increasing interest in its application to non-destructive testing (NDT). Among NDT techniques, phased array ultrasonic testing (PAUT) is an advanced extension of conventional ultrasonic testing (UT). This article provides an overview of recent research advances in ML applied to PAUT, covering key applications such as phased array ultrasonic imaging, defect detection and characterization, and data generation, with a focus on multimodal data processing and multidimensional modeling. The challenges and pathways for integrating the two techniques are examined. Finally, the article discusses the limitations of current methodologies and outlines future research directions toward more accurate, interpretable, and efficient ML-powered PAUT solutions. Full article

This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro-enterprises (SMMEs), highlighting how interconnected autonomous agents improve operational efficiency and adaptability. Using the PRISMA 2020 framework, this study systematically identified, screened, and analyzed 66 studies, including peer-reviewed and credible gray literature, published between 2019 and 2024, to assess agentic AI frameworks in SMMEs. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The results demonstrate the potential of agentic AI to address technological, ethical, and infrastructure barriers while promoting innovation, scalability, and competitiveness. This review contributes to the understanding of agentic AI frameworks by offering practical insights and setting the groundwork for further research into their applications in SMMEs’ dynamic and resource-constrained economic environments. Full article

This paper describes the E-Sense Artificial Intelligence system. It comprises a memory model with two levels of information and then a more neural layer above that. The lower memory level stores source data in a Markov (n-gram) structure that is unweighted. Then, a middle ontology level is created from a further three aggregating phases that may be deductive. Each phase re-structures from an ensemble to a tree, where the information transposition is from horizontal set-based sequences into more vertical, typed-based clusters. The base memory is essentially neutral, but bias can be added to any of the levels through associative networks. The success of the ontology typing is open to question, but the results suggested related associations more than direct ones. The third level is more functional, where each function can represent a subset of the base data and learn how to transpose across it. The functional structures are shown to be quite orthogonal, or separate, and are made from nodes with a progressive type of capability, including unordered to ordered. Comparisons with the columnar structure of the neural cortex can be made and the idea of ordinal learning, or just learning relative positions, is introduced. While this is still a work in progress, it offers a different architecture to the current frontier models and is probably one of the most biologically inspired designs. Full article

Localization is the foundation and core of autonomous driving. Current visual localization methods rely heavily on high-definition maps. However, high-definition maps are not only costly but also have poor real-time performance. In autonomous driving, place recognition is equally crucial and of great significance. Existing place recognition methods are deficient in local feature extraction and position and orientation errors can occur during the matching process. To address these limitations, this paper presents a robust multi-dimensional feature fusion framework for place recognition. Unlike existing methods such as OrienterNet, which homogenously process images and maps at the underlying feature level while neglecting modal disparities, our framework—applied to existing 2D maps—introduces a heterogeneous structural-semantic approach inspired by OrienterNet. It employs structured Stixel features (containing positional information) to capture image geometry, while representing the OSM environment through polar coordinate-based building distributions. Dedicated encoders are designed to adapt to each modality. Additionally, global relational features are generated by computing distances and angles between the current position and building pixels in the map, providing the system with detailed spatial relationship information. Subsequently, individual Stixel features are rotationally matched with global relations to achieve feature matching at diverse angles. During the BEV map matching process in OrienterNet, visual localization relies primarily on horizontal image information. In contrast, the novel method proposed herein performs matching based on vertical image information while fusing horizontal cues to complete place recognition. Extensive experimental results demonstrate that the proposed method significantly outperforms the mentioned state-of-the-art approaches in localization accuracy, effectively resolving the existing limitations. Full article

Background: Electroencephalography (EEG) assists clinicians in diagnosing epileptic seizures by recording brain electrical activity. Existing models process spatiotemporal features inefficiently either through cascaded spatiotemporal architectures or static functional connectivity, limiting their ability to capture deeper spatial–temporal correlations. Objectives: To address these limitations, we propose a Graph Transformer with Spatiotemporal Attention Fusion Gate (GT-STAFG). Methods: We analyzed 18-channel EEG data sampled at 200 Hz, transformed into the frequency domain, and segmented into 30- second windows. The graph transformer exploits dynamic graph data, while STAFG leverages self-attention and gating mechanisms to capture complex interactions by augmenting graph features with both spatial and temporal information. The clinical significance of extracted features was validated using the Integrated Gradients attribution method, emphasizing the clinical relevance of the proposed model. Results: GT-STAFG achieves the highest area under the precision–recall curve (AUPRC) scores of 0.605 on the TUSZ dataset and 0.498 on the CHB-MIT dataset, surpassing baseline models and demonstrating strong cross-patient generalization on imbalanced datasets. We applied transfer learning to leverage knowledge from the TUSZ dataset when analyzing the CHB-MIT dataset, yielding an average improvement of 8.3 percentage points in AUPRC. Conclusions: Our approach has the potential to enhance patient outcomes and optimize healthcare utilization. Full article

Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems. Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries related to course details and other essential academic information, and investigates its performance by testing it on several locally deployed large language models. By leveraging multiple LLM architectures, we evaluate performance of the models under test in terms of context length, embedding size, computational efficiency, and relevance of responses. Results: The experimental analysis obtained by this research, which builds on recent comparative studies, reveals that while larger models achieve higher relevance scores, they incur greater response times than smaller, more efficient models. Conclusions: The findings underscore the importance of balancing accuracy and efficiency for real-time educational applications. Overall, this work contributes to the field by offering insights into optimal RAG configurations and practical guidelines for deploying AI-powered educational assistants. Full article

The rapid adoption of artificial intelligence (AI) within social media platforms has fundamentally transformed the way Generation Z interacts with and navigates the digital landscape. While AI-driven algorithms enhance user experience through content personalization, they can also reinforce biases that affect the mental health and overall well-being of young individuals. This review delves into the intersections of AI bias, social medial engagement, and youth mental health, with a particular focus on how algorithmic decision-making influences exposure to harmful content, intensifies social comparison and spreads digital misinformation. By addressing these aspects, this article highlights both the risks and opportunities presented by AI-powered social media. It also advocates for evidence-based strategies to mitigate the harms associated with algorithmic bias, urging collaboration among AI developers, mental health experts, policymakers and educators at personal, community (school), and national and international levels to cultivate a safer, more supportive digital ecosystem for future generations. Full article

Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising concerns about security and regulatory compliance; (2) a lack of interpretability, where opaque AI models provide limited transparency, making it difficult for operators to trust and act on failure predictions; and (3) adaptability issues, as many existing solutions struggle to maintain a consistent performance across diverse industrial environments. Addressing these challenges requires a privacy-preserving, interpretable, and adaptive Artificial Intelligence (AI) model that ensures secure, reliable, and transparent PdM while meeting industry standards and regulatory requirements. Methods: Explainable AI (XAI) plays a crucial role in enhancing transparency and trust in PdM models by providing interpretable insights into failure predictions. Meanwhile, Federated Learning (FL) ensures privacy-preserving, decentralized model training, allowing multiple industrial sites to collaborate without sharing sensitive operational data. This proposed research developed a sustainable privacy-preserving Explainable FL (XFL) model that integrates XAI techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) into an FL structure to improve PdM’s security and interpretability capabilities. Results: The proposed XFL model enables industrial operators to interpret, validate, and refine AI-driven maintenance strategies while ensuring data privacy, accuracy, and regulatory compliance. Conclusions: This model significantly improves failure prediction, reduces unplanned downtime, and strengthens trust in AI-driven decision-making. The simulation results confirm its high reliability, achieving 98.15% accuracy with a minimal 1.85% miss rate, demonstrating its effectiveness as a scalable, secure, and interpretable solution for PdM in Industry 4.0. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly integrated into everyday life, including the complex and highly regulated healthcare sector. Given healthcare’s essential role in safeguarding human life and well-being, AI deployment requires careful oversight to ensure safety, effectiveness, and ethical compliance. This paper aims to examine the current regulatory landscapes governing AI in healthcare, particularly in the European Union (EU) and the United States (USA), and to propose practical tools to support the responsible development and implementation of AI systems. Methods: The study reviews key regulatory frameworks, ethical guidelines, and expert recommendations from international bodies, professional associations, and governmental institutions in the EU and USA. Based on this analysis, the paper develops structured questionnaires tailored for AI developers and implementers to help operationalize regulatory and ethical expectations. Results: The proposed questionnaires address critical gaps in existing frameworks by providing actionable, lifecycle-oriented tools that span AI development, deployment, and clinical use. These instruments support compliance and ethical integrity while promoting transparency and accountability. Conclusions: The structured questionnaires can serve as practical tools for health technology assessments, public procurement, accreditation processes, and training initiatives. By aligning AI system design with regulatory and ethical standards, they contribute to building trustworthy, safe, and innovative AI applications in healthcare. Full article

This paper introduces a pioneering hybrid framework that integrates Q-learning/deep Q-network (DQN) with a locally deployed large language model (LLM) to enhance obstacle avoidance in embedded robotic systems. The STM32WB55RG microcontroller handles real-time decision-making using sensor data, while a Raspberry Pi 5 computer runs a quantized TinyLlama LLM to dynamically refine navigation strategies. The LLM addresses traditional Q-learning limitations, such as slow convergence and poor adaptability, by analyzing action histories and optimizing decision-making policies in complex, dynamic environments. A selective triggering mechanism ensures efficient LLM intervention, minimizing computational overhead. Experimental results demonstrate significant improvements, including up to 41% higher deadlock recovery (81% vs. 40% for Q-learning + LLM), up to 34% faster time to goal (38 s vs. 58 s for Q-learning + LLM), and up to 14% lower collision rates (11% vs. 25% for Q-learning + LLM) compared to standalone Q-learning/DQN. This novel approach presents a solution for scalable, adaptive navigation in resource-constrained embedded robotics, with potential applications in logistics and healthcare. Full article

Artificial intelligence, the simulation of human intelligence by computers and machines, has found its way into healthcare, helping surgeons, doctors, radiologists, and many more. However, over 80% of healthcare professionals consists of people working in allied health professions such as nurses, physiotherapists, and midwives. Considering the aging of the general population around the world, the workforce shortages in these occupations are especially crucial. As the COVID-19 pandemic demonstrated, globally, most healthcare systems are strained, and there is a consensus that current healthcare systems are not sustainable with the increasing challenges. AI is often viewed as one of the potential solutions for not only reducing the strain on the healthcare workforce, but also to sustain the current workforce. Still, most AI applications are being developed for the medical community and often allied health is overlooked or not even considered despite comprising a large proportion of the total workforce. In addition, the interest of the private sector to invest specifically in the allied health workforce is low since the financial incentive is low. This paper provides examples of AI solutions for seven important allied health professions. To increase the uptake of AI solutions in allied healthcare, AI companies need to connect more with professional associations and be as patient-oriented as many claim to be. There also needs to be more AI schooling for allied healthcare professionals to increase adoption of these AI solutions. Full article

Background: Screening for substance use disorder (SUD) is a critical step to address the ongoing opioid crisis in the U.S., but fewer than 10% of people at risk screen. Technology may play a role in substantially increasing screening by making screening accessible through artificially intelligent (AI) chatbots. Methods: This was a single-arm mixed-methods pilot study to establish the system usability of an AI chatbot delivering information about substances, substance use disorder, and treatment options, and implementing self-screening for anxiety, depression, and substance use disorder. Participants were asked to engage with the AI chatbot for seven days and could self-select to screen. Results: Of the 92 participants enrolled, 91 engaged with the system at least once, and 29 (32%) completed at least one screener. Those who screened were given a referral if they exhibited moderate or severe anxiety, depression, and/or SUD. Over three-quarters (83%) of those screened received a referral for treatment, and 50% of those referred made an appointment for care. Users indicated that they found the system helpful and informative, and they felt comfortable screening. Conclusions: While other AI systems that share information about mental health and substance use exist, we know of no other AI chatbot that is being deployed specifically to facilitate SUD screening and referral. The system we describe here shows potential to support self-screening. Users generally find the system acceptable to use. AI technology may allow for improved access to SUD screening and treatment referrals, a critical step in responding to the opioid crisis. Full article

Background: Cognitive resilience is a critical factor in high-performance environments such as military operations, where sustained stress can impair attention and decision-making. In the present study, we utilized EEG and machine learning to assess cognitive resilience in elite military personnel. Methods: For this purpose, EEG signals were recorded from elite military personnel during stress-inducing attention-related and emotional tasks. The EEG signals were segmented into two temporal windows corresponding to the initial stress response (baseline) and the adaptive/recovery phase, extracting power spectral density features across delta, theta, alpha, beta, and gamma bands. Different machine learning models (Decision Tree, Random Forest, AdaBoost, XGBoost) were trained to classify temporal phases. Results: XGBoost achieved the highest accuracy (0.95), while Shapley Additive Explanations (SHAP) analysis identified delta and alpha bands (particularly in frontal and parietal regions) as key features associated with adaptive mental states. Conclusions: Our findings indicate that resilience-related neural responses can be successfully distinguished and that interpretable AI frameworks can be used for monitoring cognitive adaptation in high-stress environments. Full article

Background/Objectives: Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniques like traditional dropout. This study aims to address these limitations by proposing a more dynamic and context-sensitive dropout strategy. Methods: We introduce Probabilistic Feature Importance Dropout (PFID), a novel regularization method that assigns dropout rates based on the probabilistic significance of individual features. PFID is integrated with adaptive, structured, and contextual dropout strategies, forming a unified framework for intelligent regularization. Results: Experimental evaluation on standard benchmark datasets including CIFAR-10, MNIST, and Fashion MNIST demonstrated that PFID significantly improves performance metrics such as classification accuracy, training loss, and computational efficiency compared to conventional dropout methods. Conclusions: PFID offers a practical and scalable solution for enhancing CNN generalization and training efficiency. Its dynamic nature and feature-aware design provide a strong foundation for future advancements in adaptive regularization for deep learning models. Full article

Background: The integration of Artificial Intelligence (AI) into Business Process Management Systems (BPMSs) has led to the emergence of AI-Augmented Business Process Management Systems (ABPMSs). These systems offer dynamic adaptation, real-time process optimization, and enhanced knowledge management capabilities. However, key challenges remain, particularly regarding explainability, user engagement, and behavioral integration. Methods: This study presents a novel framework that synergistically integrates the Socialization, Externalization, Combination, and Internalization knowledge model (SECI), Agile methods (specifically Scrum), and cutting-edge AI technologies, including explainable AI (XAI), process mining, and Robotic Process Automation (RPA). The framework enables the formalization, verification, and sharing of knowledge via a well-organized, user-friendly software platform and collaborative practices, especially Communities of Practice (CoPs). Results: The framework emphasizes situation-aware explainability, modular adoption, and continuous improvement to ensure effective human–AI collaboration. It provides theoretical and practical mechanisms for aligning AI capabilities with organizational knowledge management. Conclusions: The proposed framework facilitates the transition from traditional BPMSs to more sophisticated ABPMSs by leveraging structured methodologies and technologies. The approach enhances knowledge exchange and process evolution, supported by detailed modeling using BPMN 2.0. Full article

Synthetic medical text generation has emerged as a solution to data scarcity and privacy constraints in clinical NLP. This review systematically evaluates the use of Large Language Models (LLMs) for structured medical text generation, examining techniques such as retrieval-augmented generation (RAG), structured fine-tuning, and domain-specific adaptation. Four search queries were applied following the PRISMA methodology to identify and extract data from 153 studies. Key benchmarking metrics, such as performance measures, and qualitative insights, including methodological trends and challenges, were documented. The results show that while LLM-generated text improves fluency, hallucinations and factual inconsistencies persist. Structured consultation models, such as SOAP and Calgary–Cambridge, enhance coherence but do not fully prevent errors. Hybrid techniques that combine retrieval-based grounding with domain-specific fine-tuning improve factual accuracy and task performance. Conventional evaluation metrics (e.g., ROUGE, BLEU) are insufficient for medical validation, highlighting the need for domain-specific benchmarks. Privacy-preserving strategies, including differential privacy and PHI de-identification, support regulatory compliance but may reduce linguistic quality. These findings are relevant for clinical NLP applications, such as AI-powered scribe systems, where structured synthetic datasets can improve transcription accuracy and documentation reliability. The conclusions highlight the need for balanced approaches that integrate medical structure, factual control, and privacy to enhance the usability of synthetic medical text. Full article

(1) Background: Machine unlearning plays a crucial role in privacy protection and model optimization, particularly in forgetting entire categories of data in classification tasks. However, existing methods often struggle with high computational costs, such as estimating the inverse Hessian, or require access to the original training data, limiting their practicality. (2) Methods: In this work, we introduce Scrub-and-Learn, which is a category-aware weight modification framework designed to remove class-level knowledge efficiently. By modeling unlearning as a continual learning task, our method leverages re-encoded labels of samples from the target category to guide weight updates, effectively scrubbing unwanted knowledge while preserving the rest of the model’s capacity. (3) Results and Conclusions: Experimental results on multiple benchmarks demonstrate that our method effectively eliminates targeted categories—achieving a recognition rate below $5\%$—while preserving the performance of retained classes within a $4\%$ deviation from the original model. Full article

Background: Robotic-based therapy has emerged as a prominent treatment modality for the rehabilitation of hand function impairment resulting from strokes. Aim: In this context, feature engineering becomes particularly important to estimate the intention of upper limb movements by utilizing machine learning models, especially when a hardware embedded-on-board implementation is expected, due to the strong computational, energy, and latency constraints. Methods: The present study details the implementation of four cutting-edge feature engineering techniques (random forest, minimum redundancy maximum relevance (MRMR), Davies–Bouldin index, and t-tests) in the context of machine learning algorithms (neuronal networks and bagged forests) deployed within a resource-constrained autonomous embedded system. Results: The findings of this study demonstrate that by assigning relative importance to features and removing redundant or superfluous information, it is possible to enhance the system’s execution by up to 31% while preserving the model’s performance at a comparable level. Conclusions: This work proves the usefulness of TinyML as an approach to properly integrate AI into constrained edge embedded systems to support complex strategies such as the proposed hand gesture recognition for the smart rehabilitation of post-stroke patients. Full article