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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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Biomechanics of Exoskeleton-Assisted Treadmill Walking

Biomechanics of Exoskeleton-Assisted Treadmill Walking

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： F. Di Tommaso F. Tamburella M. Lorusso L. Gastaldi M. Molinari N. L. Tagliamonte Research Unit of Advanced Robotics and Human-Centred Technologies Università Campus Bio-Medico di Roma Rome Italy Neurorehabilitation 1 Department Laboratory of Robotic Neurorehabilitation Fondazione Santa Lucia Rome Italy Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy

To exploit the benefits of treadmill-based exoskeletons, it is crucial to assess possible deviations from natural walking depending on assistive parameters. This study evaluated the biomechanics of exoskeleton-assisted treadmill walking by comparing it with free gait. Five healthy participants walked freely on a treadmill and with the assistance of the Lokomat gait trainer, while changing Body Weight Support (BWS), Gait Speed (GS), and Guidance Force (GF). Results showed that the hip and knee joint kinematics depended on BWS and GS, while changes due to GF were limited. Moreover, joint kinematics and the activity of related muscles were altered with respect to free gait, for any combination of robot parameters in the case of the ankle, and especially for low GS and with BWS in the case of hip and knee. Overall, walking with the Lokomat can mostly resemble free gait at high speed and without BWS.

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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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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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

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

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.

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

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

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.

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Estimation of the metabolic rate in the occupational field: a regression model using accelerometers

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International Journal of Industrial Ergonomics 2023年第1期96卷

作者： Falcone, Tiziana Del Ferraro, Simona Molinaro, Vincenzo Zollo, Loredana Lenzuni, Paolo Laboratory of Ergonomics and Physiology Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL via Fontana Candida 1 Monte Porzio Catone Rome Italy Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University of Rome via Álvaro del Portillo 200 Rome Italy Tuscany Regional Research Center INAIL Florence Italy

The aim of this study is to investigate the ability of the acceleration vector magnitude (VM) to predict the metabolic rate (M) for a variety of work activities. Accurate knowledge of M is of vital importance to evaluate the energy cost of work activity. While precise estimates of M can be achieved using direct calorimetry, this is not a viable option in workplaces. Ten healthy subjects (5 males, 5 females) were enrolled in six tasks simulating specific work activities. During each test, oxygen consumption rate (V˙O2), carbon dioxide production rate (V˙CO2) and accelerations at three body positions were measured. V˙O2 and V˙CO2 were used to obtain M and accelerations to calculate VM. Three mono-linear zero-intercept regression models based on center-of-mass VM were developed. A zero-intercept functional relation between M and VM was applied to the whole data set yielding the Global model. A R2 of 0.30 was obtained which indicates limited predictive ability. Two other (Vertical and Horizontal) models were also developed by separating tasks with different kinematics. With R2 = 0.56 and 0.61 they represent a significant improvement over the Global model. The results reveal a low R2 for the Global model due to collecting in the same sample many tasks that have little in common. A large R2 improvement is achieved when the dominant direction of movement is considered. The proposed method is based on a non-invasive easy-to-measure physiological parameter and can be used to track the activity level during work activity in the industrial sector. © 2023 Elsevier B.V.

关键词： Carbon dioxide

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A human-human Physical Interaction System for Training Handwriting

A Human-Human Physical Interaction System for Training Handw...

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

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

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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 Smart Solution for Proprioceptive Rehabilitation through M-IMU Sensors

A Smart Solution for Proprioceptive Rehabilitation through M...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Martina Lapresa Christian Tamantini Francesco Scotto di Luzio Francesca Cordella Marco Bravi Sandra Miccinilli Loredana Zollo Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University of Rome Rome Italy Unit of Physical Medicine and Rehabilitation Campus Bio-Medico University of Rome Rome Italy

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

The decrease of proprioception is a common consequence of neuromuscular and musculoskeletal diseases, and a rehabilitation treatment is needed to recover proprioceptive abilities. Many studies demonstrate that biofeedback training of sensory-motor functions can improve the outcome of rehabilitation. The combined use of biofeedback, wearable sensors and Internet of Things represents a very useful solution to develop smart wearable devices to be used for augmenting rehabilitation, both in clinics and at home. This paper proposes and validates a smart and easy-to-use system for proprioceptive training, which provides biofeedback to the patient thanks to a custom-designed Virtual Reality game. Attention is paid to reduce the complexity of the system and the amount of data to be exchanged for controlling the game and to be stored for assessing the patient's progresses, still ensuring the effectiveness of the system to monitor the patient also in home-rehabilitation scenarios. The system was experimentally validated on 8 healthy subjects and the obtained results demonstrated that the Virtual Reality game is easy and intuitive, as confirmed by the high performance of the subjects (success rate > 90%), and that allows training different features of limb proprioception.

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