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Gentile C, Lunghi G, Buonocore LR, Cordella F, Di Castro M, Masi A, Zollo L. Manipulation Tasks in Hazardous Environments Using a Teleoperated Robot: A Case Study at CERN. SENSORS (BASEL, SWITZERLAND) 2023; 23:1979. [PMID: 36850591 PMCID: PMC9963582 DOI: 10.3390/s23041979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/25/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
Remote robotic systems are employed in the CERN accelerator complex to perform different tasks, such as the safe handling of cables and their connectors. Without dedicated control, these kinds of actions are difficult and require the operators' intervention, which is subjected to dangerous external agents. In this paper, two novel modules of the CERNTAURO framework are presented to provide a safe and usable solution for managing optical fibres and their connectors. The first module is used to detect touch and slippage, while the second one is used to regulate the grasping force and contrast slippage. The force reference was obtained with a combination of object recognition and a look-up table method. The proposed strategy was validated with tests in the CERN laboratory, and the preliminary experimental results demonstrated statistically significant increases in time-based efficiency and in the overall relative efficiency of the tasks.
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Affiliation(s)
- Cosimo Gentile
- Centro Protesi Inail, Vigorso di Budrio, 40054 Bologna, Italy
- Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Giacomo Lunghi
- Survey, Measurement and Mechatronics (EN-SMM) Group, CERN, 1217 Geneva, Switzerland
| | | | - Francesca Cordella
- Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Mario Di Castro
- Survey, Measurement and Mechatronics (EN-SMM) Group, CERN, 1217 Geneva, Switzerland
| | - Alessandro Masi
- Survey, Measurement and Mechatronics (EN-SMM) Group, CERN, 1217 Geneva, Switzerland
| | - Loredana Zollo
- Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
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Mennella C, Alloisio S, Novellino A, Viti F. Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 22:199. [PMID: 35009742 PMCID: PMC8749695 DOI: 10.3390/s22010199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023]
Abstract
Technology-aided hand functional assessment has received considerable attention in recent years. Its applications are required to obtain objective, reliable, and sensitive methods for clinical decision making. This systematic review aims to investigate and discuss characteristics of technology-aided hand functional assessment and their applications, in terms of the adopted sensing technology, evaluation methods and purposes. Based on the shortcomings of current applications, and opportunities offered by emerging systems, this review aims to support the design and the translation to clinical practice of technology-aided hand functional assessment. To this end, a systematic literature search was led, according to recommended PRISMA guidelines, in PubMed and IEEE Xplore databases. The search yielded 208 records, resulting into 23 articles included in the study. Glove-based systems, instrumented objects and body-networked sensor systems appeared from the search, together with vision-based motion capture systems, end-effector, and exoskeleton systems. Inertial measurement unit (IMU) and force sensing resistor (FSR) resulted the sensing technologies most used for kinematic and kinetic analysis. A lack of standardization in system metrics and assessment methods emerged. Future studies that pertinently discuss the pathophysiological content and clinimetrics properties of new systems are required for leading technologies to clinical acceptance.
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Affiliation(s)
- Ciro Mennella
- Institute of Biophysics, National Research Council, Via De Marini 6, 16149 Genova, Italy; (S.A.); (F.V.)
| | - Susanna Alloisio
- Institute of Biophysics, National Research Council, Via De Marini 6, 16149 Genova, Italy; (S.A.); (F.V.)
- ETT Spa, Via Sestri 37, 16154 Genova, Italy;
| | | | - Federica Viti
- Institute of Biophysics, National Research Council, Via De Marini 6, 16149 Genova, Italy; (S.A.); (F.V.)
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Leone F, Gentile C, Ciancio AL, Gruppioni E, Davalli A, Sacchetti R, Guglielmelli E, Zollo L. Simultaneous sEMG Classification of Hand/Wrist Gestures and Forces. Front Neurorobot 2019; 13:42. [PMID: 31275131 PMCID: PMC6593108 DOI: 10.3389/fnbot.2019.00042] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 05/31/2019] [Indexed: 11/26/2022] Open
Abstract
Surface electromyography (sEMG) signals represent a promising approach for decoding the motor intention of amputees to control a multifunctional prosthetic hand in a non-invasive way. Several approaches based on proportional amplitude methods or simple thresholds on sEMG signals have been proposed to control a single degree of freedom at time, without the possibility of increasing the number of controllable multiple DoFs in a natural manner. Myoelectric control based on PR techniques have been introduced to add multiple DoFs by keeping low the number of electrodes and allowing the discrimination of different muscular patterns for each class of motion. However, the use of PR algorithms to simultaneously decode both gestures and forces has never been studied deeply. This paper introduces a hierarchical classification approach with the aim to assess the desired hand/wrist gestures, as well as the desired force levels to exert during grasping tasks. A Finite State Machine was introduced to manage and coordinate three classifiers based on the Non-Linear Logistic Regression algorithm. The classification architecture was evaluated across 31 healthy subjects. The “hand/wrist gestures classifier,” introduced for the discrimination of seven hand/wrist gestures, presented a mean classification accuracy of 98.78%, while the “Spherical and Tip force classifier,” created for the identification of three force levels, reached an average accuracy of 98.80 and 96.09%, respectively. These results were confirmed by Linear Discriminant Analysis (LDA) with time domain features extraction, considered as ground truth for the final validation of the performed analysis. A Wilcoxon Signed-Rank test was carried out for the statistical analysis of comparison between NLR and LDA and statistical significance was considered at p < 0.05. The comparative analysis reports not statistically significant differences in terms of F1Score performance between NLR and LDA. Thus, this study reveals that the use of non-linear classification algorithm, as NLR, is as much suitable as the benchmark LDA classifier for implementing an EMG pattern recognition system, able both to decode hand/wrist gestures and to associate different performed force levels to grasping actions.
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Affiliation(s)
- Francesca Leone
- Unit of Biomedical Robotics and Biomicrosystems, Universiã Bio-Medico di Roma, Rome, Italy
| | - Cosimo Gentile
- Unit of Biomedical Robotics and Biomicrosystems, Universiã Bio-Medico di Roma, Rome, Italy
| | - Anna Lisa Ciancio
- Unit of Biomedical Robotics and Biomicrosystems, Universiã Bio-Medico di Roma, Rome, Italy
| | - Emanuele Gruppioni
- Italian Workers' Compensation Authority (INAIL), Vigorso di Budrio, Bologna, Italy
| | - Angelo Davalli
- Italian Workers' Compensation Authority (INAIL), Vigorso di Budrio, Bologna, Italy
| | - Rinaldo Sacchetti
- Italian Workers' Compensation Authority (INAIL), Vigorso di Budrio, Bologna, Italy
| | - Eugenio Guglielmelli
- Italian Workers' Compensation Authority (INAIL), Vigorso di Budrio, Bologna, Italy
| | - Loredana Zollo
- Unit of Biomedical Robotics and Biomicrosystems, Universiã Bio-Medico di Roma, Rome, Italy
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Pessia P, Cordella F, Schena E, Davalli A, Sacchetti R, Zollo L. Evaluation of Pressure Capacitive Sensors for Application in Grasping and Manipulation Analysis. SENSORS (BASEL, SWITZERLAND) 2017; 17:E2846. [PMID: 29292717 PMCID: PMC5750746 DOI: 10.3390/s17122846] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 11/16/2017] [Accepted: 12/05/2017] [Indexed: 11/26/2022]
Abstract
The analysis of the human grasping and manipulation capabilities is paramount for investigating human sensory-motor control and developing prosthetic and robotic hands resembling the human ones. A viable solution to perform this analysis is to develop instrumented objects measuring the interaction forces with the hand. In this context, the performance of the sensors embedded in the objects is crucial. This paper focuses on the experimental characterization of a class of capacitive pressure sensors suitable for biomechanical analysis. The analysis was performed in three loading conditions (Distributed load, 9 Tips load, and Wave-shaped load, thanks to three different inter-elements) via a traction/compression testing machine. Sensor assessment was also carried out under human- like grasping condition by placing a silicon material with the same properties of prosthetic cosmetic gloves in between the sensor and the inter-element in order to simulate the human skin. Data show that the input-output relationship of the analyzed, sensor is strongly influenced by both the loading condition (i.e., type of inter-element) and the grasping condition (with or without the silicon material). This needs to be taken into account to avoid significant measurement error. To go over this hurdle, the sensors have to be calibrated under each specific condition in order to apply suitable corrections to the sensor output and significantly improve the measurement accuracy.
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Affiliation(s)
- Paola Pessia
- Unit of Biomedical Robotics and Biomicrosystems, University Campus Bio-Medico of Rome, via Alvaro del Portillo 21, 00128 Rome, Italy.
| | - Francesca Cordella
- Unit of Biomedical Robotics and Biomicrosystems, University Campus Bio-Medico of Rome, via Alvaro del Portillo 21, 00128 Rome, Italy.
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, University Campus Bio-Medico of Rome, via Alvaro del Portillo 21, 00128 Rome, Italy.
| | - Angelo Davalli
- Centro Protesi INAIL, Via Rabuina 14, 40054 Budrio (BO), Italy.
| | | | - Loredana Zollo
- Unit of Biomedical Robotics and Biomicrosystems, University Campus Bio-Medico of Rome, via Alvaro del Portillo 21, 00128 Rome, Italy.
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Romeo RA, Cordelia F, Davalli A, Sacchetti R, Guglielmelli E, Zollo L. An instrumented object for studying human grasping. IEEE Int Conf Rehabil Robot 2017; 2017:1031-1036. [PMID: 28813957 DOI: 10.1109/icorr.2017.8009385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper proposes the use of an instrumented object for the study of the human grasping strategies. The proposed object is able to measure the grasping forces by means of three Force Sensitive Resistor (FSR) sensors and triaxial acceleration through an accelerometer. The object orientation angles (roll and pitch) can be estimated from the accelerometer output in quasi-static condition, whereas slippage events can be detected through the Power Spectrum Density (PSD) computation of the acceleration on at least one of the three axes. An experimental session on 7 healthy subjects has been performed; each subject used the instrumented object to perform 8 tripod grasp trials. All the sensory information, i.e. applied forces, object orientation and slippage, have been analyzed in order to evaluate the grasping strategies of the different subjects.
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Slippage Detection with Piezoresistive Tactile Sensors. SENSORS 2017; 17:s17081844. [PMID: 28796170 PMCID: PMC5579946 DOI: 10.3390/s17081844] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 08/01/2017] [Accepted: 08/07/2017] [Indexed: 11/25/2022]
Abstract
One of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset.
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Cordella F, Corato FD, Siciliano B, Zollo L. A stochastic algorithm for automatic hand pose and motion estimation. Med Biol Eng Comput 2017; 55:2197-2208. [PMID: 28593507 DOI: 10.1007/s11517-017-1654-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Accepted: 04/27/2017] [Indexed: 10/19/2022]
Abstract
In this paper, a novel, robust, and simple method for automatically estimating the hand pose is proposed and validated. The method uses a multi-camera optoelectronic system and a model-based stochastic algorithm. The approach is marker-based and relies on an Unscented Kalman Filter. A hand kinematic model is introduced for constraining relative marker's positions and improving the algorithm robustness with respect to outliers and possible occlusions. The algorithm outputs are 3D coordinate measures of markers and hand joint angle values. To validate the proposed algorithm, a comparison with ground truths for angular and 3D coordinate measures is carried out. The comparative analysis shows the advantages of using the model-based stochastic algorithm with respect to standard processing software of optoelectronic cameras in terms of implementation simplicity, time consumption, and user effort. The accuracy is remarkable, with a difference of maximum 0.035r a d and 4m m with respect to angular and 3D Cartesian coordinates ground truths, respectively.
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Affiliation(s)
- Francesca Cordella
- Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, via Alvaro del Portillo 21, 00128, Rome, Italy.
| | | | - Bruno Siciliano
- PRISMA Lab, Department of Electrical Engineering and Information Technology, Università di Napoli Federico II, via Claudio 21, 80125, Naples, Italy
| | - Loredana Zollo
- Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, via Alvaro del Portillo 21, 00128, Rome, Italy
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Cordella F, Taffoni F, Raiano L, Carpino G, Pantoni M, Zollo L, Schena E, Guglielmelli E, Formica D. Design and development of a sensorized cylindrical object for grasping assessment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:3366-3369. [PMID: 28269025 DOI: 10.1109/embc.2016.7591449] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Aim of this work is to design and develop an instrumented cylindrical object equipped with force sensors, which is able to assess grasping performance of both human and robotic hands. The object is made of two concentric shells between which sixteen piezoresistive sensors have been located in order to measure the forces applied by the hand fingers during grasping. Furthermore, a magneto-inertial unit has been positioned inside the object for acquiring information about object orientation during manipulation. A wireless communication between the electronic boards, responsible for acquiring the data from the sensors, and a remote laptop has been guaranteed. The object has been conceived in such a way to be adopted for evaluating both power and precision grasps and for measuring the forces applied by each finger of the hand. In order to evaluate object performance, a finite element analysis for estimating the deformation of the external shell for different force values has been carried out. Moreover, to evaluate object sensitivity, a static analysis of the force transmitted by the external shell to the underlying sensors has been performed by varying the thickness of the shells. The obtained preliminary results have validated the feasibility of using the developed object for assessing grasping performed by human and robotic hands.
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