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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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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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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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Deep Learning for human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative Study

arXiv

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arXiv 2025年

作者： Coser, Omar Tamantini, Christian Tortora, Matteo Furia, Leonardo Sicilia, Rosa Zollo, Loredana Soda, Paolo Unit of Computer Systems & Bioinformatics Università Campus Bio-Medico di Roma Via Álvaro del Portillo 21 Rome00128 Italy Unit of Advanced Robotics and Human-Centered Technologies Università Campus Bio-Medico di Roma Via Álvaro del Portillo 21 Rome00128 Italy Dipartimento di Ingegneria Navale Elettrica Elettronica e delle Telecomunicazioni Università degli Studi di Genova Via all’Opera Pia 11a Genova16145 Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Universitetstorget 4 Umeå40196 Sweden Institute of Cognitive Sciences and Technologies National Research Council of Italy Rome00196 Italy

Wearable robotics for lower-limb assistance have become a pivotal area of research, aiming to enhance mobility for individuals with physical impairments or augment the performance of able-bodied users. Accurate and adaptive control systems are essential to ensure seamless interaction between the wearer and the robotic device, particularly when navigating diverse and dynamic terrains. Despite the recent advances in neural networks for time series analysis, no attempts have been directed towards the classification of ground conditions, categorized into five classes and subsequently determining the ramp’s slope and stair’s height. In this respect, this paper presents an experimental comparison between eight deep neural network backbones to predict high-level locomotion parameters across diverse terrains. All the models are trained on the publicly available CAMARGO 2021 dataset. IMU-only data equally or outperformed IMU+EMG inputs, promoting a cost-effective and efficient design. Indeeds, using three IMU sensors, the LSTM achieved high terrain classification accuracy (0.94 ± 0.04) and precise ramp slope (1.95 ± 0.58°) and the CNN-LSTM a stair height (15.65 ± 7.40 mm) estimations. As a further contribution, SHAP analysis justified sensor reduction without performance loss, ensuring a lightweight setup. The system operates with 2 ms inference time, supporting real-time applications. The code is code available at https://***/cosbidev/human-Locomotion-Identification. © 2025, CC BY.

关键词： Adaptive control systems

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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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Muscle Fatigue Detection and Analysis Using EMG Sensor 7

Muscle Fatigue Detection and Analysis Using EMG Sensor

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7th IEEE International Conference on Engineering technologies and Applied Sciences, ICETAS 2020

作者： Ahmed, Syed Faiz Joyo, M. Kamran Mahdi, Hussain Falih Kiwarkis, Isho James American University of Kurdistan Elec. and Tele. Engineering Duhok Iraq Università Campus Bio-Medico Unit of Advanced Robotics and Human-centred Technologies Rome Italy University of Diyala Faculty of Engineering Computer Engineering Department Iraq

ISBN: (纸本)9780738105048

Electromyography (EMG) is a sensor to diagnose the health of muscle with the placement of electrodes through the surface of the skin. Nowadays, muscular disorder occurs where the problem is the cause of muscle weakness, pain, fatigue, and also due to paralysis. Biomedical applications where the electromyography (EMG) sensor is used to collect data of a normal muscles contradiction, and there are two classifiers which are linear discriminant analysis (LDA) and support vector machines (SVM) that are prompt to undergo with the process and also mean absolute value feature (MAV) traces are used to extract from the electromyography (EMG). Ascertain part of body muscle targeted with the different subject with certain body mass index (BMI), the movement contradiction of the muscles through the sensor pad electrodes that are attached to the human body where the point of muscles that wanted to observe and the data collected or being used. The study provides guidelines for the prediction of muscle fatigue using electromyography (EMG) sensors with classification methods that are approved. © 2020 IEEE.

关键词： Muscle

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A Hybrid Position/Force Control for Robot-Aided Pedicle Tapping in Spinal Surgery

A Hybrid Position/Force Control for Robot-Aided Pedicle Tapp...

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

作者： Rosaura Morfino Clemente Lauretti Francesca Cordella Loredana Zollo Unit of Advanced Robotics and Human-Centred Technologies (CREO lab) Università Campus Bio-Medico Rome Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Pedicle screw fixation is a widely adopted surgical technique to enhance column stability and promote spinal fusion. Robot-assisted spinal surgery represents a solution to the challenges associated with the tapping phase of this procedure, which requires high precision and can cause surgeon fatigue. Based on the promising results of Authors' previous study, which achieved an improvement in surgeons' overall physical comfort for robot-aided pedicle tapping, this study seeks to improve the intervention effectiveness and efficiency, and enhance at the same time the robotic system usability and acceptability, thereby contributing to increased patient safety and enhanced surgeon's comfort. To accomplish these goals, a new semi-autonomous control strategy based on a hybrid position/force control is proposed. This control strategy offers two operation modes that can be selected according to the surgeon's preference: pushing mode and torquing mode. Eleven subjects were recruited, and an ad hoc experimental setup and protocol were implemented. After a training phase, the subjects performed the tapping procedure on an anthropomorphic spine phantom. The study reveals that both operation modes are equally effective (error from the optimal tapping depth: $0.8\pm 0.4$ [mm]) and efficient, comparable to traditional robot-assisted pedicle tapping, and significantly improve ergonomics (rapid upper limb assessment score: $2.65\pm 0.75$ ). The torquing mode reduces muscular fatigue more than the pushing mode. Despite subjects' preference for the torquing mode, the possibility to choose between the two maximizes system usability and human-robot interaction acceptability by 18.8%.

关键词： Training Protocols Surgery Phantoms human-robot interaction Fasteners Fatigue Safety Usability Robots

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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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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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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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