Publications​

@article{krilavicius_extended_2025,

title = {eXtended Reality and Artificial Intelligence in Medicine and Rehabilitation},

author = {Tomas Krilavičius and Lucio Tommaso De Paolis and Valerio De Luca and Josef Spjut},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217159420&doi=10.1007%2fs10796-025-10580-8&partnerID=40&md5=3bc0bae0925d3f2f6d1a6e1e659b9aae},

doi = {10.1007/s10796-025-10580-8},

issn = {13873326},

year  = {2025},

date = {2025-01-01},

journal = {Information Systems Frontiers},

abstract = {This special issue focuses on the application of eXtended Reality (XR) technologies—comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—and Artificial Intelligence (AI) in the fields of medicine and rehabilitation. AR provides support in minimally invasive surgery, where it visualises internal anatomical structures on the patient’s body and provides real-time feedback to improve accuracy, keep the surgeon’s attention and reduce the risk of errors. Furthermore, XR technologies can be used to develop applications for pre-operative planning or for training surgeons through serious games. AI finds applications both in medical image processing, for the recognition of anatomical structures and the reconstruction of 3D models, and in the analysis of biological data for patient monitoring and disease diagnosis. In rehabilitation, XR and AI can enable personalised therapy plans, increase patient engagement through immersive environments and provide real-time feedback to improve recovery outcomes. The papers in this special issue deal with rehabilitation through serious games, AI-enhanced XR applications for healthcare, digital twins and the analysis of bio/neuro-adaptive signals. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.},

keywords = {3D modeling, Artificial Intelligence, Augmented Reality, Extended reality, Minimally-invasive surgery, Personalized medicine, Pre-operative planning, Surgery},

pubstate = {published},

tppubtype = {article}

}

@article{agostinelli_validation_2024,

title = {Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments},

author = {Thomas Agostinelli and Andrea Generosi and Silvia Ceccacci and Maura Mengoni},

url = {https://www.nature.com/articles/s41598-024-79373-4},

doi = {10.1038/s41598-024-79373-4},

issn = {2045-2322},

year  = {2024},

date = {2024-11-01},

urldate = {2024-12-28},

journal = {Scientific Reports},

volume = {14},

number = {1},

pages = {27785},

abstract = {This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.},

keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0},

pubstate = {published},

tppubtype = {article}

}

@article{recupitoTechnicalDebtAIenabled2024,

title = {Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture},

author = {Gilberto Recupito and Fabiano Pecorelli and Gemma Catolino and Valentina Lenarduzzi and Davide Taibi and Dario Di Nucci and Fabio Palomba},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121224001961},

doi = {10.1016/j.jss.2024.112151},

issn = {0164-1212},

year  = {2024},

date = {2024-10-01},

urldate = {2024-07-07},

journal = {Journal of Systems and Software},

volume = {216},

pages = {112151},

abstract = {Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems' quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems' quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers' awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method: We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results: The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion: We conclude the article by distilling lessons learned and actionable insights for researchers.},

keywords = {Artificial Intelligence, Software Engineering, Technical Debt Management},

pubstate = {published},

tppubtype = {article}

}

@article{mennella_promoting_2024,

title = {Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare},

author = {Ciro Mennella and Massimo Esposito and Giuseppe De Pietro and Umberto Maniscalco},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482524009119},

doi = {10.1016/j.compbiomed.2024.108826},

issn = {00104825},

year  = {2024},

date = {2024-09-01},

urldate = {2024-07-21},

journal = {Computers in Biology and Medicine},

volume = {179},

pages = {108826},

abstract = {Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance. 

The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis. 

Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations. 

This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.},

keywords = {Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series},

pubstate = {published},

tppubtype = {article}

}

@article{melillo_synchronization_2024,

title = {Synchronization of a Virtual Reality Scenario to Uterine Contractions for Labor Pain Management: Development Study and Randomized Controlled Trial},

author = {Antonio Melillo and Sarah Rachedi and Giuseppe Caggianese and Luigi Gallo and Patrizia Maiorano and Francesca Gimigliano and Fabio Lucidi and Giuseppe De Pietro and Maurizio Guida and Antonio Giordano and Andrea Chirico},

url = {https://www.liebertpub.com/doi/10.1089/g4h.2023.0202},

doi = {10.1089/g4h.2023.0202},

issn = {2161-783X, 2161-7856},

year  = {2024},

date = {2024-06-01},

urldate = {2024-07-21},

journal = {Games for Health Journal},

pages = {g4h.2023.0202},

abstract = {Background: Labor is described as one of the most painful events women can experience through their lives, and labor pain shows unique features and rhythmic fluctuations. 

Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity. 

Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor. 

Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls. 

Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.},

keywords = {Artificial Intelligence, Labor, Pain, User study, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{raikovAcceleratingHumanComputer2024,

title = {Accelerating human–computer interaction through convergent conditions for LLM explanation},

author = {Aleksandr Raikov and Alberto Giretti and Massimiliano Pirani and Luca Spalazzi and Meng Guo},

url = {https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1406773/full},

doi = {10.3389/frai.2024.1406773},

issn = {2624-8212},

year  = {2024},

date = {2024-05-01},

urldate = {2024-10-05},

journal = {Frontiers in Artificial Intelligence},

volume = {7},

abstract = {<p>The article addresses the accelerating human–machine interaction using the large language model (LLM). It goes beyond the traditional logical paradigms of explainable artificial intelligence (XAI) by considering poor-formalizable cognitive semantical interpretations of LLM. XAI is immersed in a hybrid space, where humans and machines have crucial distinctions during the digitisation of the interaction process. The author's convergent methodology ensures the conditions for making XAI purposeful and sustainable. This methodology is based on the inverse problem-solving method, cognitive modeling, genetic algorithm, neural network, causal loop dynamics, and eigenform realization. It has been shown that decision-makers need to create unique structural conditions for information processes, using LLM to accelerate the convergence of collective problem solving. The implementations have been carried out during the collective strategic planning in situational centers. The study is helpful for the advancement of explainable LLM in many branches of economy, science and technology.</p>},

note = {Publisher: Frontiers},

keywords = {Artificial Intelligence, Causal Loop Dynamics, Cognitive semantics, Eigenform, Explainable AI, Hybrid reality, LLM},

pubstate = {published},

tppubtype = {article}

}

@article{jamali_facial_2024,

title = {Facial Expression Recognition for Measuring Jurors’ Attention in Acoustic Jury Tests},

author = {Reza Jamali and Andrea Generosi and Josè Yuri Villafan and Maura Mengoni and Leonardo Pelagalli and Gianmarco Battista and Milena Martarelli and Paolo Chiariotti and Silvia Angela Mansi and Marco Arnesano and Paolo Castellini},

url = {https://www.mdpi.com/1424-8220/24/7/2298},

doi = {10.3390/s24072298},

issn = {1424-8220},

year  = {2024},

date = {2024-04-01},

urldate = {2024-12-28},

journal = {Sensors},

volume = {24},

number = {7},

pages = {2298},

abstract = {The perception of sound greatly impacts users’ emotional states, expectations, affective relationships with products, and purchase decisions. Consequently, assessing the perceived quality of sounds through jury testing is crucial in product design. However, the subjective nature of jurors’ responses may limit the accuracy and reliability of jury test outcomes. This research explores the utility of facial expression analysis in jury testing to enhance response reliability and mitigate subjectivity. Some quantitative indicators allow the research hypothesis to be validated, such as the correlation between jurors’ emotional responses and valence values, the accuracy of jury tests, and the disparities between jurors’ questionnaire responses and the emotions measured by FER (facial expression recognition). Specifically, analysis of attention levels during different statuses reveals a discernible decrease in attention levels, with 70 percent of jurors exhibiting reduced attention levels in the ‘distracted’ state and 62 percent in the ‘heavy-eyed’ state. On the other hand, regression analysis shows that the correlation between jurors’ valence and their choices in the jury test increases when considering the data where the jurors are attentive. The correlation highlights the potential of facial expression analysis as a reliable tool for assessing juror engagement. The findings suggest that integrating facial expression recognition can enhance the accuracy of jury testing in product design by providing a more dependable assessment of user responses and deeper insights into participants’ reactions to auditory stimuli.},

keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition},

pubstate = {published},

tppubtype = {article}

}

@article{dubbioso_precision_2024,

title = {Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment},

author = {Raffaele Dubbioso and Myriam Spisto and Laura Verde and Valentina Virginia Iuzzolino and Gianmaria Senerchia and Giuseppe De Pietro and Ivanoe De Falco and Giovanna Sannino},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1746809423011394},

doi = {10.1016/j.bspc.2023.105706},

issn = {17468094},

year  = {2024},

date = {2024-03-01},

urldate = {2024-07-21},

journal = {Biomedical Signal Processing and Control},

volume = {89},

pages = {105706},

abstract = {Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motorneurons of the bulbar, cervical, thoracic, or lumbar segments. Bulbar presentation is a devastating characteristic that impairs patients’ ability to communicate and is linked to shorter survival. Early acoustic manifestation of voice symptoms, such as dysarthria, is very variable, making its detection and classification challenging, both by human specialists and automatic systems. In this context, precision medicine, defined as “prevention and treatment strategies that take individual variability into account”, has gained a great interest in the ALS community. Specifically, the use of innovative Artificial Intelligence techniques, such as Machine Learning, plays a pivotal role in finding specific patterns in the data set to help neurologists in clinical decision-making. Therefore, the main objective of this study was to find new markers, and new patterns, to promptly detect the possible presence of dysarthria and to correctly classify its severity. We have performed an acoustic analysis on different voice signals of various degrees of impairment acquired during outpatient visits at the ALS center of the “Federico II” University Hospital. From the collected signals, a new database containing different acoustic parameters was realized, on which several experiments were performed. The study led us to the discovery of markers that helped to develop a decision tree that separated healthy subjects from patients and, among patients, those with different severity of dysarthria. This model achieved good results in terms of dysarthria classification accuracy, 86.6%, which is excellent considering the small number of subjects in the data set.},

keywords = {Amyotrophic lateral sclerosis (ALS), Artificial Intelligence, Bulbar functions, Classification, Dysarthria, Precision medicine},

pubstate = {published},

tppubtype = {article}

}

@article{pontilloMachineLearningbasedTest2024,

title = {Machine learning-based test smell detection},

author = {Valeria Pontillo and Dario Amoroso d'Aragona and Fabiano Pecorelli and Dario Di Nucci and Filomena Ferrucci and Fabio Palomba},

url = {https://link.springer.com/10.1007/s10664-023-10436-2},

doi = {10.1007/s10664-023-10436-2},

issn = {1382-3256, 1573-7616},

year  = {2024},

date = {2024-03-01},

urldate = {2024-07-07},

journal = {Empirical Software Engineering},

volume = {29},

number = {2},

pages = {55},

abstract = {Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.},

keywords = {Artificial Intelligence, Code Smell Detection, Software Engineering, Technical Debt Management},

pubstate = {published},

tppubtype = {article}

}

@article{mennella_ethical_2024,

title = {Ethical and regulatory challenges of AI technologies in healthcare: A narrative review},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2405844024023284},

doi = {10.1016/j.heliyon.2024.e26297},

issn = {24058440},

year  = {2024},

date = {2024-02-01},

urldate = {2024-07-21},

journal = {Heliyon},

volume = {10},

number = {4},

pages = {e26297},

abstract = {Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. 

A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. 

This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.},

keywords = {Artificial Intelligence, Decision-Making, Ethics, Healthcare, Regulatory guidelines, Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{barbareschi_investigating_2024,

title = {Investigating the Resilience Source of Classification Systems for Approximate Computing Techniques},

author = {Mario Barbareschi and Salvatore Barone},

url = {https://ieeexplore.ieee.org/document/10542568},

doi = {10.1109/TETC.2024.3403757},

issn = {2168-6750},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Transactions on Emerging Topics in Computing},

pages = {12},

abstract = {During the last decade, classification systems (CSs) received significant research attention, with new learning algorithms achieving high accuracy in various applications. However, their resource-intensive nature, in terms of hardware and computation time, poses new design challenges. 

CSs exhibit inherent error resilience, due to redundancy of training sets, and self-healing properties, making them suitable for Approximate Computing (AxC). 

AxC enables efficient computation by using reduced precision or approximate values, leading to energy, time, and silicon area savings. 

Exploiting AxC involves estimating the introduced error for each approximate variant found during a Design-Space Exploration (DSE). This estimation has to be both rapid and meaningful, considering a substantial number of test samples, which are utterly conflicting demands. 

In this paper, we investigate on sources of error resiliency of CSs, and we propose a technique to haste the DSE that reduces the computational time for error estimation by systematically reducing the test set. In particular, we cherry-pick samples that are likely to be more sensitive to approximation and perform accuracy-loss estimation just by exploiting such a sample subset. 

In order to demonstrate its efficacy, we integrate our technique into two different approaches for generating approximate CSs, showing an average speed-up up to approx18.},

keywords = {Artificial Intelligence, Classification systems, Neural networks, Tree Ensemble},

pubstate = {published},

tppubtype = {article}

}

@article{agnolucciQualityAwareImageTextAlignment2024,

title = {Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment},

author = {Lorenzo Agnolucci and Leonardo Galteri and Marco Bertini},

url = {https://arxiv.org/abs/2403.11176},

doi = {10.48550/ARXIV.2403.11176},

year  = {2024},

date = {2024-01-01},

journal = {arXiv preprint arXiv:2403.11176},

abstract = {No-Reference Image Quality Assessment (NR-IQA) focuses on designing methods to measure image quality in alignment with human perception when a high-quality reference image is unavailable. The reliance on annotated Mean Opinion Scores (MOS) in the majority of state-of-the-art NR-IQA approaches limits their scalability and broader applicability to real-world scenarios. To overcome this limitation, we propose QualiCLIP (Quality-aware CLIP), a CLIP-based self-supervised opinion-unaware method that does not require labeled MOS. In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate representations that correlate with the inherent quality of the images. Starting from pristine images, we synthetically degrade them with increasing levels of intensity. Then, we train CLIP to rank these degraded images based on their similarity to quality-related antonym text prompts, while guaranteeing consistent representations for images with comparable quality. Our method achieves state-of-the-art performance on several datasets with authentic distortions. Moreover, despite not requiring MOS, QualiCLIP outperforms supervised methods when their training dataset differs from the testing one, thus proving to be more suitable for real-world scenarios. Furthermore, our approach demonstrates greater robustness and improved explainability than competing methods. The code and the model are publicly available at https://github.com/miccunifi/QualiCLIP.},

note = {arXiv: 2403.11176 

tex.copyright: All rights reserved},

keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Image Processing, Quality Assessment, Self-Supervised Learning, Vision and Language},

pubstate = {published},

tppubtype = {article}

}

@article{scannapieco_cnosso_2024,

title = {Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction},

author = {Simone Scannapieco and Claudio Tomazzoli},

url = {https://www.sciencedirect.com/science/article/pii/S0957417423035406},

doi = {10.1016/j.eswa.2023.123038},

issn = {0957-4174},

year  = {2024},

date = {2024-01-01},

journal = {Expert Systems with Applications},

volume = {245},

pages = {123038},

abstract = {The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models – either used alone or in combination – for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the information, whereas model-based frameworks adopt a semantic, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.},

keywords = {Artificial Intelligence, Document automation, Information Extraction, Natural language analysis, Natural Language Processing, Semantic Role Labeling},

pubstate = {published},

tppubtype = {article}

}

@article{tomazzoli_cobot_2024,

title = {A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying},

author = {Claudio Tomazzoli and Andrea Ponza and Matteo Cristani and Francesco Olivieri and Simone Scannapieco},

url = {https://www.mdpi.com/2076-3417/14/9/3777},

doi = {10.3390/app14093777},

issn = {2076-3417},

year  = {2024},

date = {2024-01-01},

journal = {Applied Sciences},

volume = {14},

number = {9},

abstract = {Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.},

keywords = {Artificial Intelligence, Collaborative robotic, Computer vision, Cyber-Physical Systems, Deep Learning, Machine Learning, Precision agriculture},

pubstate = {published},

tppubtype = {article}

}

@article{mennella_generating_2023,

title = {Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523011307},

doi = {10.1016/j.compbiomed.2023.107665},

issn = {00104825},

year  = {2023},

date = {2023-12-01},

urldate = {2024-07-21},

journal = {Computers in Biology and Medicine},

volume = {167},

pages = {107665},

abstract = {Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation. 

This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises. 

A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90. 

This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.},

keywords = {Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data},

pubstate = {published},

tppubtype = {article}

}