Robotics

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30 pages, 10077 KiB

Open AccessArticle

Design and Experimental Evaluation of Multiple 3D-Printed Reduction Gearboxes for Wearable Exoskeletons

by Riccardo Bezzini, Giulia Bassani, Carlo Alberto Avizzano and Alessandro Filippeschi

Robotics 2024, 13(11), 168; https://doi.org/10.3390/robotics13110168 - 19 Nov 2024

Viewed by 429

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression [...] Read more.

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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15 pages, 2870 KiB

Open AccessArticle

Towards Prosthesis Control: Identification of Locomotion Activities through EEG-Based Measurements

by Saqib Zafar, Hafiz Farhan Maqbool, Muhammad Imran Ashraf, Danial Javaid Malik, Zain ul Abdeen, Wahab Ali, Juri Taborri and Stefano Rossi

Robotics 2024, 13(9), 133; https://doi.org/10.3390/robotics13090133 - 1 Sep 2024

Viewed by 1079

Abstract

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework [...] Read more.

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework for the recognition and classification of locomotion activities using electroencephalography (EEG) data by comparing the performance of different machine learning algorithms. Data of the lower limb movements during level ground walking as well as going up stairs, down stairs, up ramps, and down ramps were collected from 10 healthy volunteers. Time- and frequency-domain features were extracted by applying independent component analysis (ICA). Successively, they were used to train and test random forest and k-nearest neighbors (kNN) algorithms. For the classification, random forest revealed itself as the best-performing one, achieving an overall accuracy up to 92%. The findings of this study contribute to the field of assistive robotics by confirming that EEG-based measurements, when combined with appropriate machine learning models, can serve as robust inputs for prosthesis control systems. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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24 pages, 8524 KiB

Open AccessArticle

Reducing Hand Kinematics by Introducing Grasp-Oriented Intra-Finger Dependencies

by Tomislav Bazina, Goran Mauša, Saša Zelenika and Ervin Kamenar

Robotics 2024, 13(6), 82; https://doi.org/10.3390/robotics13060082 - 21 May 2024

Viewed by 1155

Abstract

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing [...] Read more.

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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23 pages, 7567 KiB

Open AccessArticle

A Framework for Modeling, Optimization, and Musculoskeletal Simulation of an Elbow–Wrist Exosuit

by Ali KhalilianMotamed Bonab, Domenico Chiaradia, Antonio Frisoli and Daniele Leonardis

Robotics 2024, 13(4), 60; https://doi.org/10.3390/robotics13040060 - 6 Apr 2024

Viewed by 2163

Abstract

The light weight and compliance of exosuits are valuable benefits not present rigid exoskeleton devices, yet these intriguing features make it challenging to properly model and simulate their interaction with the musculoskeletal system. Tendon-driven exosuits adopt an electrical motor combined with pulleys and [...] Read more.

The light weight and compliance of exosuits are valuable benefits not present rigid exoskeleton devices, yet these intriguing features make it challenging to properly model and simulate their interaction with the musculoskeletal system. Tendon-driven exosuits adopt an electrical motor combined with pulleys and cable transmission in the actuation stage. An important aspect of the design of these systems for the load transfer efficacy and comfort of the user is the anchor point positioning. In this paper, we propose a framework, whose first purpose is as a design methodology for the synthesis of an exosuit device, achieved by optimizing the anchor point location. The optimization procedure finds the best 3D position of the anchor points based on the interaction forces between the exosuit and the upper arm. The computation of the forces is based on the combination of a mathematical model of a wrist–elbow exosuit and a dynamic model of the upper arm. Its second purpose is the simulation of the kinematic and physiological effects of the interaction between the arm, the exosuit, and the complex upper limb musculoskeletal system. It offers insights into muscular and exoskeleton loading during operation. The presented experiments involve the development and validation of personalized musculoskeletal models, with kinematic, anthropometric, and electromyographic data measured in a load-lifting task. Simulation of the exosuit operation—coupled with the musculoskeletal model—showed the efficacy of the suit in assisting the wrist and elbow muscles and provided interesting highlights about the impact of the assistance on shoulder muscles. Finally, we provide a possible design of an elbow and wrist exosuit based on the optimized results. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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13 pages, 837 KiB

Open AccessArticle

Investigation of the Effectiveness of the Robotic ReStore Soft Exoskeleton in the Development of Early Mobilization, Walking, and Coordination of Stroke Patients: A Randomized Clinical Trial

by Szilvia Kóra, Adrienn Bíró, Nándor Prontvai, Mónika Androsics, István Drotár, Péter Prukner, Tamás Haidegger, Klaudia Széphelyi and József Tollár

Robotics 2024, 13(3), 44; https://doi.org/10.3390/robotics13030044 - 5 Mar 2024

Cited by 1 | Viewed by 3235

Abstract

Medical robotics nowadays can prevent, treat, or alleviate numerous severe conditions, including the dire consequences of stroke. Our objective was to determine the effect of employing a robotic soft exoskeleton in therapy on the development of the early mobilization, gait, and coordination in [...] Read more.

Medical robotics nowadays can prevent, treat, or alleviate numerous severe conditions, including the dire consequences of stroke. Our objective was to determine the effect of employing a robotic soft exoskeleton in therapy on the development of the early mobilization, gait, and coordination in stroke patients. The ReStore™ Soft Exo-Suit, a wearable exosuit developed by a leading company with exoskeleton technology, was utilized. It is a powered, lightweight device intended for use in stroke rehabilitation for people with lower limb disability. We performed a randomized clinical intervention, using a before–after trial design in a university hospital setting. A total of 48 patients with a history of stroke were included, of whom 39 were randomized and 30 completed the study. Interventions: Barthel Index and modified Rankin scale (mRS) patients were randomly assigned to a non-physical intervention control (n = 9 of 39 completed, 30 withdrew before baseline testing), or to a high-intensity agility program (15 sessions, 5 weeks, n = 30 completed). The main focus of assessment was on the Modified Rankin Scale. Additionally, we evaluated secondary factors including daily life functionality, five dimensions of health-related quality of life, the Beck depression inventory, the 6 min walk test (6MWT), the Berg Balance Scale (BBS), and static balance (center of pressure). The Robot-Assisted Gait Therapy (ROB/RAGT) program led to significant improvements across various measures, including a 37% improvement in Barthel Index scores, a 56% increase in 10 m walking speed, and a 68% improvement in 6 min walking distance, as well as notable enhancements in balance and stability. Additionally, the intervention group demonstrated significant gains in all these aspects compared to the control group. In conclusion, the use of robotic therapy can be beneficial in stroke rehabilitation. These devices support the restoration and improvement of movement in various ways and contribute to restoring balance and stability. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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29 pages, 13682 KiB

Open AccessArticle

Design and Control of the Rehab-Exos, a Joint Torque-Controlled Upper Limb Exoskeleton

by Domenico Chiaradia, Gianluca Rinaldi, Massimiliano Solazzi, Rocco Vertechy and Antonio Frisoli

Robotics 2024, 13(2), 32; https://doi.org/10.3390/robotics13020032 - 17 Feb 2024

Viewed by 2338

Abstract

This work presents the design of the Rehab-Exos, a novel upper limb exoskeleton designed for rehabilitation purposes. It is equipped with high-reduction-ratio actuators and compact elastic joints to obtain torque sensors based on strain gauges. In this study, we address the torque sensor [...] Read more.

This work presents the design of the Rehab-Exos, a novel upper limb exoskeleton designed for rehabilitation purposes. It is equipped with high-reduction-ratio actuators and compact elastic joints to obtain torque sensors based on strain gauges. In this study, we address the torque sensor performances and the design aspects that could cause unwanted non-axial moment load crosstalk. Moreover, a new full-state feedback torque controller is designed by modeling the multi-DOF, non-linear system dynamics and providing compensation for non-linear effects such as friction and gravity. To assess the proposed upper limb exoskeleton in terms of both control system performances and mechanical structure validation, the full-state feedback controller was compared with two other benchmark-state feedback controllers in both a transparency test—ten subjects, two reference speeds—and a haptic rendering evaluation. Both of the experiments were representative of the intended purpose of the device, i.e., physical interaction with patients affected by limited motion skills. In all experimental conditions, our proposed joint torque controller achieved higher performances, providing transparency to the joints and asserting the feasibility of the exoskeleton for assistive applications. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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26 pages, 5465 KiB

Open AccessArticle

NOHAS: A Novel Orthotic Hand Actuated by Servo Motors and Mobile App for Stroke Rehabilitation

by Ebenezer Raj Selvaraj Mercyshalinie, Akash Ghadge, Nneka Ifejika and Yonas Tadesse

Robotics 2023, 12(6), 169; https://doi.org/10.3390/robotics12060169 - 8 Dec 2023

Cited by 2 | Viewed by 4255

Abstract

The rehabilitation process after the onset of a stroke primarily deals with assisting in regaining mobility, communication skills, swallowing function, and activities of daily living (ADLs). This entirely depends on the specific regions of the brain that have been affected by the stroke. [...] Read more.

The rehabilitation process after the onset of a stroke primarily deals with assisting in regaining mobility, communication skills, swallowing function, and activities of daily living (ADLs). This entirely depends on the specific regions of the brain that have been affected by the stroke. Patients can learn how to utilize adaptive equipment, regain movement, and reduce muscle spasticity through certain repetitive exercises and therapeutic interventions. These exercises can be performed by wearing soft robotic gloves on the impaired extremity. For post-stroke rehabilitation, we have designed and characterized an interactive hand orthosis with tendon-driven finger actuation mechanisms actuated by servo motors, which consists of a fabric glove and force-sensitive resistors (FSRs) at the tip. The robotic device moves the user’s hand when operated by mobile phone to replicate normal gripping behavior. In this paper, the characterization of finger movements in response to step input commands from a mobile app was carried out for each finger at the proximal interphalangeal (PIP), distal interphalangeal (DIP), and metacarpophalangeal (MCP) joints. In general, servo motor-based hand orthoses are energy-efficient; however, they generate noise during actuation. Here, we quantified the noise generated by servo motor actuation for each finger as well as when a group of fingers is simultaneously activated. To test ADL ability, we evaluated the device’s effectiveness in holding different objects from the Action Research Arm Test (ARAT) kit. Our device, novel hand orthosis actuated by servo motors (NOHAS), was tested on ten healthy human subjects and showed an average of 90% success rate in grasping tasks. Our orthotic hand shows promise for aiding post-stroke subjects recover because of its simplicity of use, lightweight construction, and carefully designed components. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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17 pages, 3772 KiB

Open AccessArticle

A Semiautonomous Control Strategy Based on Computer Vision for a Hand–Wrist Prosthesis

by Gianmarco Cirelli, Christian Tamantini, Luigi Pietro Cordella and Francesca Cordella

Robotics 2023, 12(6), 152; https://doi.org/10.3390/robotics12060152 - 13 Nov 2023

Cited by 3 | Viewed by 2432

Abstract

Alleviating the burden on amputees in terms of high-level control of their prosthetic devices is an open research challenge. EMG-based intention detection presents some limitations due to movement artifacts, fatigue, and stability. The integration of exteroceptive sensing can provide a valuable solution to [...] Read more.

Alleviating the burden on amputees in terms of high-level control of their prosthetic devices is an open research challenge. EMG-based intention detection presents some limitations due to movement artifacts, fatigue, and stability. The integration of exteroceptive sensing can provide a valuable solution to overcome such limitations. In this paper, a novel semiautonomous control system (SCS) for wrist–hand prostheses using a computer vision system (CVS) is proposed and validated. The SCS integrates object detection, grasp selection, and wrist orientation estimation algorithms. By combining CVS with a simulated EMG-based intention detection module, the SCS guarantees reliable prosthesis control. Results show high accuracy in grasping and object classification (≥97%) at a fast frame analysis frequency (2.07 FPS). The SCS achieves an average angular estimation error ≤18° and stability ≤0.8° for the proposed application. Operative tests demonstrate the capabilities of the proposed approach to handle complex real-world scenarios and pave the way for future implementation on a real prosthetic device. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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