Kinematic Design Optimization of an Eight Degree-of-Freedom Upper-Limb Exoskeleton

Zeiaee, Amin; Soltani-Zarrin, Rana; Langari, Reza; Tafreshi, Reza

doi:10.1017/s0263574719001085

Cited by 22 publications

(11 citation statements)

References 49 publications

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“…In addition, w 1 and w 2 presented as the weighted values of the error rate of the classification model and the rRMSE value of the regression model, respectively. Weights are set as 0.7 and 0.3, respectively [39]. The maximum optimization iteration was set to 100.…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

et al. 2021

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Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.

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Section: Hyperparameter Optimizationmentioning

confidence: 99%

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

et al. 2021

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“…Starting from [37], where the shoulder is simplified to a ball and socket joint, plenty of kinematic configurations have been proposed and analyzed. Most of these utilize serial chain manipulators and adopt a 3 Degrees of Freedom (DoFs) design for the Glenohumeral joint, where the axes of the 3 Revolute (R) joints must intersect the human shoulder center of rotation [3,4,[38][39][40][41][42]. Other configurations employ 4R [43,44] or 5R [34] redundant schematics with the aim of overcoming pos-Fig.…”

Section: Introductionmentioning

confidence: 99%

Conceptual design and virtual prototyping of a wearable upper limb exoskeleton for assisted operations

Bilancia

Berselli

2021

Int J Interact Des Manuf

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This paper introduces a novel upper limb robotic exoskeleton designed to assist industrial operators in a wide range of manual repetitive tasks, such as tool handling and lifting/moving of heavy items. Due to its reduced size and high maneuverability, the proposed portable device may also be employed for rehabilitation purposes (e.g. as an aid for people with permanent neuromuscular diseases or post-stroke patients). Its primary function is to compensate the gravity loads acting on the human shoulder by means of a hybrid system consisting of four electric motors and three passive springs. The paper focuses on the exoskeleton mechanical design and virtual prototyping. After a preliminary review of the existent architectures and procedures aimed at defining the exoskeleton functional requirements, a detailed behavioral analysis is conducted using analytical and numerical approaches. The developed interactive model allows to simulate both kinematics and statics of the exoskeleton for every possible movement within the design workspace. To validate the model, the results have been compared with the ones achieved with a commercial multibody software for three different operator’s movements.

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“…The CLEVERarm is an 8-DOFs lightweight and ergonomic upper-limb rehabilitation exoskeleton for upper-limb impairment developed at the Texas A&M University, capable of producing diverse and perceptually rich training scenarios Soltani-Zarrin et al (2017b) ; Zeiaee et al (2019) . The robot supports the motion of the shoulder girdle, glenohumeral joint, elbow, and wrist.…”

Section: Available Exoskeleton Prototypesmentioning

confidence: 99%

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons

et al. 2021

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Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) “high-level” training modalities, 2) “low-level” control strategies, and 3) “hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.

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Kinematic Design Optimization of an Eight Degree-of-Freedom Upper-Limb Exoskeleton

Cited by 22 publications

References 49 publications

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

Conceptual design and virtual prototyping of a wearable upper limb exoskeleton for assisted operations

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons

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