A Real Framework to Apply Collaborative Robots in Upper Limb Rehabilitation

Fernandes, Lucas de Azevedo; Brito, Thadeu; Piardi, Luis; Lima, José; Leitão, Paulo

doi:10.5220/0009004501760183

“…According to an analysis of the previously mentioned articles, the GUI is implemented in all of the proposed architecture except for [ 18 , 19 , 35 ]; the EMG sensor is not implemented in the majority of the discussed papers [ 18 , 19 , 30 , 31 , 34 , 36 , 37 ]. Furthermore, there is a discrepancy between the rehabilitation strategies, none of the systems presented are able to offer both the possibility to choose between default exercises and the possibility to create exercises and adapt, i.e., either to follow a pre-defined rehabilitation pathway or to create an exercise that the robot will reproduce.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, some works use UR3 as a development platform. These works are mainly those of Fernandes et al [ 18 , 19 ], where a simulation for rehabilitation using UR3 was proposed, and a Reinforcement Learning algorithm was implemented to provide resistance force based on the patient’s needs. In [ 37 ], a study of rehabilitation using virtual reality was performed using the UR3 to provide physical support and haptic feedback.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, others operate in a three-dimensional (3D) environment with 3 Degrees of Freedom (DOF) [ 14 ], 4 DOF [ 15 ], 5 DOF [ 9 , 16 ], 6 DOF [ 12 ], and a few even reached 7 DOF or more [ 17 ]. Some research teams have focused on developing algorithms for using dedicated commercial robots in the industrial domain [ 18 , 19 ]. As an example of a developed rehabilitation robotic, ANYexo [ 20 , 21 ] is an upper limb rehabilitation exoskeleton robot designed with 6 DOF and a six-series elastic actuator; it has an improved design that allows for a wide range of motion.…”

Section: Introductionmentioning

confidence: 99%

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Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

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Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost.

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“…The recent advances in robot technologies are opening new development pathways that are of great interest to the medical sector, especially for palliative and supportive care [15] as well as for rehabilitation actions [4,6]. Mainly two solutions of rehabilitative robots are well established in clinical applications, namely grounded exoskeletons and grounded end-effector devices [8].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, dedicated approaches are required to design the proper rehabilitative setup. Interesting proposals arise from the literature [4,6] but general design procedures that support the engineer with the definition of Cobot based layouts and the integration with other technologies are still missing. By choosing a Cobot to perform rehabilitation tasks, the main issues are related to their limited payload and the low stiffness, which change within the workspace.…”

Section: Introductionmentioning

confidence: 99%

Workspace Placement of Motion Trajectories by Manipulability Index for Optimal Design of Cobot Assisted Rehabilitation Solutions

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Onfiani²,

Pini³

et al. 2022

CADandA

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Nowadays the rehabilitation process involves the patient and the therapist, that must interact to recover the motion of limbs and the strength of related muscles to restore the initial functionalities. The therapy relies on the experience and sensitivity of the therapist that identifies the rehabilitation exercises which are necessary to recover the expected ability. To prevent inappropriate practices an interesting aid may come by mixing collaborative robots, namely Cobots, and additive manufacturing technologies. The proper integration of a Cobot assistant and custom-printed training objects enables a significant improvement in the effectiveness of the therapy action and the related user experience since the programmed trajectories can mimic the movements related to activities of daily living. To this aim, this work describes an integrated approach to support the design of Cobot assisted rehabilitative solutions. The object selected by the patient and therapist, the motion pattern, the clamping area, and loads on the limb represents the design requirements. The motion trajectories defining the specific training tasks are the starting point to the optimal placement within the Cobot workspace. Specifically, manipulability maps can provide an objective evaluation of the locations where the exercises are performed at the best of workspace and configuration of the Cobot. A simple upper limb rehabilitation exercise based on a demonstrative handle has been selected to prove the effectiveness of the proposed approach. The results confirm that the manipulability index can be adopted to drive the preliminary design of the Cobotic solution toward a feasible configuration.

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A Digital Workflow for the Design and Additive Manufacturing of Multi-thickness Dental Aligners

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Nowadays the rehabilitation process involves the patient and the therapist, that must interact to recover the motion of limbs and the strength of related muscles to restore the initial functionalities. The therapy relies on the experience and sensitivity of the therapist that identifies the rehabilitation exercises which are necessary to recover the expected ability. To prevent inappropriate practices an interesting aid may come by mixing collaborative robots, namely Cobots, and additive manufacturing technologies. The proper integration of a Cobot assistant and custom-printed training objects enables a significant improvement in the effectiveness of the therapy action and the related user experience since the programmed trajectories can mimic the movements related to activities of daily living. To this aim, this work describes an integrated approach to support the design of Cobot assisted rehabilitative solutions. The object selected by the patient and therapist, the motion pattern, the clamping area, and loads on the limb represents the design requirements. The motion trajectories defining the specific training tasks are the starting point to the optimal placement within the Cobot workspace. Specifically, manipulability maps can provide an objective evaluation of the locations where the exercises are performed at the best of workspace and configuration of the Cobot. A simple upper limb rehabilitation exercise based on a demonstrative handle has been selected to prove the effectiveness of the proposed approach. The results confirm that the manipulability index can be adopted to drive the preliminary design of the Cobotic solution toward a feasible configuration.

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A Real Framework to Apply Collaborative Robots in Upper Limb Rehabilitation

Cited by 3 publications

References 0 publications

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

Workspace Placement of Motion Trajectories by Manipulability Index for Optimal Design of Cobot Assisted Rehabilitation Solutions

A Digital Workflow for the Design and Additive Manufacturing of Multi-thickness Dental Aligners

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