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11th Italian Workshop on Artificial Intelligence and robotics, AIRO 2024

作者： Cirelli, Gianmarco Tamantini, Christian Cordella, Luigi Pietro Cordella, Francesca Unit of Advanced Robotics and Human-Centered Technologies Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome Italy Institute of Cognitive Sciences and Technologies National Research Council of Italy Via Giandomenico Romagnosi 18a Rome Italy University of Naples Federico II Via Claudio 21 Naples80125 Italy

Artificial Intelligence-based Semiautonomous control strategies (AI-based SCS) could significantly improve the reliability and naturalness of prosthetic hand control. The integration of a computer vision system (CVS) and the user motion intention allows the prosthetic hand to autonomously recognize the object to be grasped and to select the appropriate hand posture, with the user in charge of initiating the execution of the grasp. In this work, an AI-based SCS integrating EMG signals with a CVS is presented. The control strategy was assessed both in laboratory settings and in more complex real life scenarios. The results show strong performance of the proposed SCS with an Accuracy in Object and Grasp Classification above 97%, a Mean Time of Execution of 0.483s, and a Mean Angular Error and Estimation Stability in wrist orientation of 16.26 ± 8.62° and 0.2, respectively. The control strategy effectively managed also complex situations, achieving a 100% Success Rate in the conducted tests. © 2025 Copyright for this paper by its authors.

关键词： Knee prostheses

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Slippage Classification in Prosthetic Hands with a Spiking Neural Network 9th

Slippage Classification in Prosthetic Hands with a Spikin...

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9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Follmann, Jone Gentile, Cosimo Cordella, Francesca Zollo, Loredana Rodrigues, Cesar Ramos Biomedical Engineering Institute/PPGEEL Federal University of Santa Catarina Florianópolis Brazil Centro Protesi INAIL Budrio Italy Research Unit of Advanced Robotics and Human-Centred Technologies Università Campus Bio-Medico di Roma Rome Italy

ISBN: (纸本)9783031494062

Poliarticulated myoelectric upper limb prostheses have greatly enhanced dexterity and integration possibilities to patients’ skeletal, muscular and even neural structures. Every manipulation task has, as elemental conditions, touch and slippage identification. As an alternative to the algorithmic approach to slippage detection, we investigate the implementation of spiking neural network aiming the future integration of tactile feedback into hand prosthesis. To this end, we trained a spiking neural network for identifying slippage events during grasping performed by the IH2 Azzurra robotic hand. A comparison with the results obtained from the authors in a previous work is made. In addition to the inherent advantages of the spiking neural network approach, such as biological plausibility and potential ultra-low power hardware implementation, an accuracy of 100% was achieved in the classification of slip and non-slip events. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Low power electronics

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Validating ViSCOPE: Vital Signs Contactless Estimation Pipeline for Robot-Aided Rehabilitation

Validating ViSCOPE: Vital Signs Contactless Estimation Pipel...

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2024 Workshop on advanced AI Methods and Interfaces for human-centered Assistive and Rehabilitation robotics, AIxIA F4MR WS 2024

作者： Molle, Rita Tamantini, Christian Taffoni, Fabrizio Caroppo, Andrea Manni, Andrea Siciliano, Pietro Aleardo Zollo, Loredana Leone, Alessandro Research Unit of Advanced Robotics and Human-Centred Technologies Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy Institute of Cognitive Sciences and Technologies National Research Council of Italy Via Giandomenico Romagnosi 18a Rome00196 Italy Institute for Microelectronics and Microsystems National Research Council of Italy Via per Monteroni c/o Campus Universitario Palazzina A3 Lecce73100 Italy

In rehabilitation settings, accurate monitoring of physiological parameters is critical for tailoring therapeutic interventions and ensuring patient safety. This paper introduces ViSCOPE, a contactless vital sign estimation pipeline, and its integration into a robotic rehabilitation platform, aimed at providing non-invasive monitoring of heart rate, breathing rate, and oxygen saturation. ViSCOPE was validated against gold-standard devices in both resting conditions and after physical exertion, simulating the demands of rehabilitation exercises. In resting conditions, the system achieved mean absolute errors of 5.50 ± 4.91 bpm for heart rate, 5.13 ± 2.86 bpm for breathing rate, and 1.23 ± 0.43% for oxygen saturation. However, after physical activity, the error committed in estimating the heart rate significantly increased up to 13.10 ± 8.79 bpm (p © 2024 Copyright for this paper by its authors.

关键词： Heart

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Contactless Respiration Rate Monitoring and human Body Pose Detection for Search and Rescue Robots

Contactless Respiration Rate Monitoring and Human Body Pose ...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Roberto Cittadini Luca Rosario Buonocore Mario Di Castro Loredana Zollo European Council for Nuclear Research CERN Meyrin Switzerland Research Unit of Advanced Robotics and Human-Centred Technologies Universita Campus Bio-Medico di Roma Rome Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

The significant increase of robots in complex and hazardous environments highlights their ability to timely and effectively respond to emergency and life-threatening situations. The integration of robotics with unobtrusive monitoring of vital signals emerges as a promising avenue to improve the well-being and safety of workers. Respiration rate is a crucial parameter to instant assess the health state of survivors and to guide Search and Rescue (SAR) operations in challenging emergencies. Although existing robotic solutions in the literature for the detection of respiration parameters in emergency scenarios propose various solutions involving cameras, radar, and physical interaction with survivors, however, the current solutions neglect the impact of body posture on the measurement of respiration rate and the need to adapt the robotic configuration according to body position, compromising the accuracy of the measurement. A case study concerns the European Organisation for Nuclear research (CERN), which faces unique challenges in emergency response due to its extensive underground facilities. This work proposes an innovative contactless respiration rate monitoring approach, that considers human body posture and utilizes a laser sensor's optical operating principles. A mobile robotic system is employed to detect missing individuals and to point a laser sensor on the survivor's chest-abdominal area for measuring respiration rate. Optimal sensor placement enhances the performance of monitoring algorithms in challenging conditions such as darkness or smoke presence. To validate the proposed approach, an experiment was conducted with ten healthy volunteers in an environment simulating the real scenario. Respiration rate values under different body postures and different breathing conditions are contactless measured via laser and compared with a benchmark device.

关键词： Accuracy Current measurement Area measurement Measurement by laser beam Biomedical measurement Position measurement Robot sensing systems Optical sensors Robots Monitoring

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Plati, Daphni K. Konstantakopoulos, Fotios S. Kalatzis, Theofanis Manousos, Dimitrios Kassiotis, Thomas Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Forth Institute of Computer Science Crete HeraklionGR 70013 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy

ISBN: (纸本)9798350383386

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition;(ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital. © 2023 IEEE.

关键词： Hospitals

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Towards AI-driven Next Generation Personalized Healthcare and Well-being

Towards AI-driven Next Generation Personalized Healthcare an...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Aksu, Fatih Bria, Alessandro Caragliano, Alice Natalina Caruso, Camillo Maria Chen, Wenting Cordelli, Ermanno Coser, Omar Francesconi, Arianna Furia, Leonardo Guarrasi, Valerio Iannello, Giulio Lauretti, Clemente Manni, Guido Marino, Giustino Paolo, Domenico Ruffini, Filippo Shen, Linlin Sicilia, Rosa Soda, Paolo Tamantini, Christian Tortora, Matteo Wu, Zhuoru Zollo, Loredana Department of Biomedical Sciences Humanitas University Milan Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Cassino Italy Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy City University of Hong Kong Hong Kong Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Campus Bio-Medico of Rome Italy Shenzhen University China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Sweden

In the last few years Artificial Intelligence (AI) is emerging as a game changer in many areas of society and, in particular, its integration in medicine heralds a transformative approach towards personalized healthcare and well-being, promising significant improvements in diagnostic precision, therapeutic outcomes, and patient care. Our research explores the cutting-edge realms of multimodal AI, resilient AI, and healthcare robotics, aiming to harness the synergy of diverse data modalities and advanced computational models to redefine healthcare paradigms. This multidisciplinary effort seeks to bridge technology and clinical practice, advancing AI-driven next generation personalized healthcare and well-being. © 2024 Copyright for this paper by its authors.

关键词： Personalized medicine

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Assessing the "rule of 10" in Orthopedic Robot-Aided Rehabilitation for Tailoring Exercise Dose

Assessing the "rule of 10" in Orthopedic Robot-Aided Rehabil...

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2024 Workshop on advanced AI Methods and Interfaces for human-centered Assistive and Rehabilitation robotics, AIxIA F4MR WS 2024

作者： Molle, Rita Tamantini, Christian Lauretti, Clemente Cordella, Francesca Scotto Di Luzio, Francesco Sebastiani, Davide Santacaterina, Fabio Bravi, Marco Bressi, Federica Miccinilli, Sandra Zollo, Loredana Research Unit of Advanced Robotics and Human-Centred Technologies Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy Institute of Cognitive Sciences and Technologies National Research Council of Italy Via Giandomenico Romagnosi 18a Rome00196 Italy Rehabilitation Unit Fondazione Policlinico Universitario Campus Bio-Medico Via Alvaro del Portillo 200 Rome00128 Italy Research Unit of Physical Medicine and Rehabilitation Universitá Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy

Robot-aided rehabilitation has gained attention for its ability to deliver standardized, repeatable therapeutic interventions. A key but underexplored aspect of these protocols is determining the optimal therapy dose, often defined by the number of repetitions. This study examines the "Rule of 10,"a guideline used by physiotherapists to adjust exercise intensity based on patient-reported pain and exertion, recommending that the Cumulative Perceived Strain (CPS) should not exceed 10 to avoid overstrain. Eight orthopedic patients participated in a robot-aided rehabilitation session using a KUKA Lightweight Robot 4+ controlled by a tunable interaction controller. The relationship between the number of repetitions performed and clinical scales was examined across different CPS values. The results demonstrated that CPS = 11 returned strong statistically significant correlations (ρ = 0.79 and ρ = -0.75 for CMS and DASH, respectively). This indicates that 11 may be a more suitable threshold for customising therapy intensity. Future research should endeavour to refine therapy protocols by integrating real-time assessments of strain and clinical conditions. © 2024 Copyright for this paper by its authors.

关键词： Orthopedics

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HeAL9000: An Intelligent Rehabilitation Robot

HeAL9000: An Intelligent Rehabilitation Robot

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2021 Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help?, SMARTERCARE 2021

作者： Cristofori, Lorenzo Hromei, Claudiu D. Di Luzio, Francesco Scotto Tamantini, Christian Cordella, Francesca Croce, Danilo Zollo, Loredana Basili, Roberto Department of Enterprise Engineering University of Rome Tor Vergata Rome Italy Research Unit of Advanced Robotics and Human-Centred Technologies Universitá Campus Bio-Medico di Roma Rome Italy

AI applications to health related processes include the adoption of robotic platforms for rehabilitation, aiming at the delivery of highly intensive, repeatable and accurate motion therapies and able to constantly monitor the patient and provide the suitable assistance levels. However, a comprehensive approach to robot-aided rehabilitation requires also a social level of interaction with the patient that implies cognitive modeling and linguistic communication. It is worth noticing that robotic platforms providing both physical and cognitive support to patients have not been proposed so far. In the HeAL9000 project an intelligent robot for rehabilitation of patients affected by musculoskeletal disorders is proposed with cognitive and linguistic abilities. It relies strongly on machine learning technologies whose aim is to support cost effective engineering of the platform as well as evolving capabilities across time. In the paper early experimental evidence is acquired through quantitative evaluation. © 2021 Copyright for this paper by its authors.

关键词： human robot interaction

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BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications

arXiv

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arXiv 2024年

作者： Manni, Guido Lauretti, Clemente Prata, Francesco Papalia, Rocco Zollo, Loredana Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Unit of Advanced Robotics and Human-Centred Technologies Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Urology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy

Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization and Mapping (MVSLAM) has emerged as a promising solution, its implementation in endoscopic procedures faces significant challenges due to hardware limitations, such as the use of a monocular camera and the absence of odometry sensors. This study presents BodySLAM, a robust deep learning-based MVSLAM approach that addresses these challenges through three key components: CycleVO, a novel unsupervised monocular pose estimation module;the integration of the state-of-the-art Zoe architecture for monocular depth estimation;and a 3D reconstruction module creating a coherent surgical map. The approach is rigorously evaluated using three publicly available datasets (Hamlyn, EndoSLAM, and SCARED) spanning laparoscopy, gastroscopy, and colonoscopy scenarios, and benchmarked against four state-of-the-art methods. Results demonstrate that CycleVO exhibited competitive performance with the lowest inference time among pose estimation methods, while maintaining robust generalization capabilities, whereas Zoe significantly outperformed existing algorithms for depth estimation in endoscopy. BodySLAM’s strong performance across diverse endoscopic scenarios demonstrates its potential as a viable MVSLAM solution for endoscopic applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Depth perception

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