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Lapresa M, Lauretti C, Scotto di Luzio F, Bressi F, Santacaterina F, Bravi M, Guglielmelli E, Zollo L, Cordella F. Development and Validation of a System for the Assessment and Recovery of Grip Force Control. Bioengineering (Basel) 2023; 10:63. [PMID: 36671635 PMCID: PMC9854469 DOI: 10.3390/bioengineering10010063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/28/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
The ability to finely control hand grip forces can be compromised by neuromuscular or musculoskeletal disorders. Therefore, it is recommended to include the training and assessment of grip force control in rehabilitation therapy. The benefits of robot-mediated therapy have been widely reported in the literature, and its combination with virtual reality and biofeedback can improve rehabilitation outcomes. However, the existing systems for hand rehabilitation do not allow both monitoring/training forces exerted by single fingers and providing biofeedback. This paper describes the development of a system for the assessment and recovery of grip force control. An exoskeleton for hand rehabilitation was instrumented to sense grip forces at the fingertips, and two operation modalities are proposed: (i) an active-assisted training to assist the user in reaching target force values and (ii) virtual reality games, in the form of tracking tasks, to train and assess the user's grip force control. For the active-assisted modality, the control of the exoskeleton motors allowed generating additional grip force at the fingertips, confirming the feasibility of this modality. The developed virtual reality games were positively accepted by the volunteers and allowed evaluating the performance of healthy and pathological users.
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Affiliation(s)
- Martina Lapresa
- Research Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Clemente Lauretti
- Research Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Francesco Scotto di Luzio
- Research Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Federica Bressi
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Fabio Santacaterina
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Marco Bravi
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Eugenio Guglielmelli
- Research Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Loredana Zollo
- Research Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
| | - Francesca Cordella
- Research Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
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Lapresa M, Zollo L, Cordella F. A user-friendly automatic toolbox for hand kinematic analysis, clinical assessment and postural synergies extraction. Front Bioeng Biotechnol 2022; 10:1010073. [PMID: 36440447 PMCID: PMC9686293 DOI: 10.3389/fbioe.2022.1010073] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/27/2022] [Indexed: 12/07/2023] Open
Abstract
The clinical assessment of the human hand is typically conducted through questionnaires or tests that include objective (e.g., time) and subjective (e.g., grasp quality) outcome measures. However, there are other important indicators that should be considered to quantify grasp and movement quality in addition to the time needed by a subject to execute a task, and this is essential for human and artificial hands that attempt to replicate the human hand properties. The correct estimation of hand kinematics is fundamental for computing these indicators with high fidelity, and a technical background is typically required to perform this analysis. In addition, to understand human motor control strategies as well as to replicate them on artificial devices, postural synergies were widely explored in recent years. Synergies should be analyzed not only to investigate possible modifications due to musculoskeletal and/or neuromuscular disorders, but also to test biomimetic hands. The aim of this work is to present an open source toolbox to perform all-in-one kinematic analysis and clinical assessment of the hand, as well as to perform postural synergies extraction. In the example provided in this work, the tool takes as input the position of 28 retroreflective markers with a diameter of 6 mm, positioned on specific anatomical landmarks of the hand and recorded with an optoelectronic motion capture system, and automatically performs 1) hand kinematic analysis (i.e., computation of 23 joint angles); 2) clinical assessment, by computing indicators that allow quantifying movement efficiency (Peak Grip Aperture), smoothness (Normalized Dimensionless Jerk Grasp Aperture) and speed (Peak Velocity of Grasp Aperture), planning capabilities (Time to Peak Grip Aperture), spatial posture (Wrist and Finger Joint Angles) and grasp stability (Posture of Hand Finger Joints), and 3) postural synergies extraction and analysis through the Pareto, Scree and Loadings plots. Two examples are described to demonstrate the applicability of the toolbox: the first one aiming at performing a clinical assessment of a volunteer and the second one aiming at extracting and analyzing the volunteer's postural synergies. The tool allows calculating joint angles with high accuracy (reconstruction errors below 4 mm and 3.2 mm for the fingers and wrist respectively) and automatically performing clinical assessment and postural synergies extraction. Results can be visually inspected, and data can be saved for any desired post processing analysis. Custom-made protocols to extract joint angles, based on different markersets, could be also integrated in the toolbox. The tool can be easily exploitable in clinical contexts, as it does not require any particular technical knowledge to be used, as confirmed by the usability evaluation conducted (perceived usability = 94.2 ± 5.4). In addition, it can be integrated with the SynGrasp toolbox to perform grasp analysis of underactuated virtual hands based on postural synergies.
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Affiliation(s)
- Martina Lapresa
- Department of Engineering, Research Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Roma, Italy
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Lauretti C, Cordella F, Ciancio AL, Trigili E, Catalan JM, Badesa FJ, Crea S, Pagliara SM, Sterzi S, Vitiello N, Garcia Aracil N, Zollo L. Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons. Front Neurorobot 2018; 12:5. [PMID: 29527161 PMCID: PMC5829101 DOI: 10.3389/fnbot.2018.00005] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 01/31/2018] [Indexed: 11/13/2022] Open
Abstract
The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.
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Affiliation(s)
- Clemente Lauretti
- Research Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, Rome, Italy
| | - Francesca Cordella
- Research Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, Rome, Italy
| | - Anna Lisa Ciancio
- Research Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, Rome, Italy
| | - Emilio Trigili
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Jose Maria Catalan
- Biomedical Neuroengineering Research Group, Miguel Hernandez University, Elche, Spain
| | - Francisco Javier Badesa
- Departamento de Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores, Universidad de Cádiz, Cádiz, Spain
| | - Simona Crea
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | | | - Silvia Sterzi
- Unit of Physical and Rehabilitation Medicine, Università Campus Bio-Medico, Rome, Italy
| | - Nicola Vitiello
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Nicolas Garcia Aracil
- Biomedical Neuroengineering Research Group, Miguel Hernandez University, Elche, Spain
| | - Loredana Zollo
- Research Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, Rome, Italy
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Noccaro A, Cordella F, Zollo L, Di Pino G, Guglielmelli E, Formica D. A teleoperated control approach for anthropomorphic manipulator using magneto-inertial sensors. RO-MAN ... : THE ... IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION : PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION 2017; 2017:156-161. [PMID: 30949293 DOI: 10.1109/roman.2017.8172295] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper we propose and validate a teleoperated control approach for an anthropomorphic redundant robotic manipulator, using magneto-inertial sensors (IMUs). The proposed method allows mapping the motion of the human arm (used as the master) on the robot end-effector (the slave). We record arm movements using IMU sensors, and calculate human forward kinematics to be mapped on robot movements. In order to solve robot kinematic redundancy, we implemented different algorithms for inverse kinematics that allows imposing anthropomorphism criteria on robot movements. The main objective is to let the user to control the robotic platform in an easy and intuitive manner by providing the control input freely moving his/her own arm and exploiting redundancy and anthropomorphism criteria in order to achieve human-like behaviour on the robot arm. Therefore, three inverse kinematics algorithms are implemented: Damped Least Squares (DLS), Elastic Potential (EP) and Augmented Jacobian (AJ). In order to evaluate the performance of the algorithms, four healthy subjects have been asked to control the motion of an anthropomorphic robot arm (i.e. the Kuka Light Weight Robot 4+) through four magneto-inertial sensors (i.e. Xsens Wireless Motion Tracking sensors - MTw) positioned on their arm. Anthropomorphism indices and position and orientation errors between the human hand pose and the robot end-effector pose were evaluated to assess the performance of our approach.
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Affiliation(s)
- A Noccaro
- Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy
| | - F Cordella
- Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy
| | - L Zollo
- Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy
| | - G Di Pino
- Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy
| | - E Guglielmelli
- Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy
| | - D Formica
- Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering and with the Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico di Roma, via Alvaro del Portillo 21, 00128, Rome, Italy
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