Wearable Elbow Exoskeleton Actuated with Shape Memory Alloy

Copaci, Dorin; Flores, Antonio; Molina‐Rueda, Francisco; Blanco, Dolores; Moreno, Lourdes

doi:10.1007/978-3-319-46669-9_79

Cited by 23 publications

(33 citation statements)

References 4 publications

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“…Previous publications [3,5] presented a low-level control algorithm based on a BPID (Bilinear Proportional Integral Derivative) controller, which governs the SMA-based exoskeleton in position. Their algorithm, involving the position and temperature sensors, is capable to do data acquisition from the sensors or control the exoskeleton in flexion, extension, or in flexion-extension using an antagonistic controller (two BPID controllers in a parallel configuration [5]).…”

Section: The High-level Control Algorithmmentioning

confidence: 99%

“…Their algorithm, involving the position and temperature sensors, is capable to do data acquisition from the sensors or control the exoskeleton in flexion, extension, or in flexion-extension using an antagonistic controller (two BPID controllers in a parallel configuration [5]). With the data acquisition configuration, the SMA-based exoskeleton only offers the possibility to diagnostic and evaluate the patient.…”

Section: The High-level Control Algorithmmentioning

confidence: 99%

“…A wearable shape memory alloy (SMA) exoskeleton with two degrees of freedom (DOF) (for flexion-extension and pronation-supination), actuated with SMAs was presented in [3]. In that work, the control algorithm gave the possibility to control the exoskeleton tracking a reference for passive rehabilitation therapy, in flexion [4], only actuating in flexion and recuperating (during the extension movement) with the aid of gravity, and actuating with two SMA-based actuators in flexion and extension [5].…”

Section: Introductionmentioning

confidence: 99%

“…In that work, the control algorithm gave the possibility to control the exoskeleton tracking a reference for passive rehabilitation therapy, in flexion [4], only actuating in flexion and recuperating (during the extension movement) with the aid of gravity, and actuating with two SMA-based actuators in flexion and extension [5]. The reference pattern in both cases represents a repetitive movement (for example a sinusoidal trajectory) defined by the therapist, which made the rehabilitation passive.…”

Section: Introductionmentioning

confidence: 99%

“…If the actuator operates with higher forces than those specified, the lifetime drops to only a few thousand cycles. Regarding applying the necessary torque to execute defined movements (the necessary torque of each movement was found from a biomechanical simulation [3]), a summary of the system configuration of the actuators can be seen in the Table 2. …”

mentioning

confidence: 99%

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A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

Copaci¹,

Serrano²,

Moreno³

et al. 2018

Preprint

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Abstract:A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) has been designed. It is applied to a shape memory alloy (SMA) actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according data collected online during the first seconds of therapy sessions. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the position reference pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm has been tested in simulations and with healthy people for control of an elbow exoskeleton in flexion-extension movements. The results indicate that sEMG signals from elbow muscles in combination with pressure sensors that measure arm-exoskeleton interaction can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according a patient's intention.

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Section: The High-level Control Algorithmmentioning

confidence: 99%

Section: The High-level Control Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

Copaci¹,

Serrano²,

Moreno³

et al. 2018

Preprint

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Rigidity‐Tunable Materials for Soft Engineering Systems

Roh,

Lim,

Kang

et al. 2024

Adv Eng Mater

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Engineering systems that leverage the flexibility and softness of soft materials have been fostering revolutionary progress and broad interest across various applications. The inherently flexible mechanical properties of these materials lay the groundwork for engineering systems that can adapt comparably to biological organisms, enabling them to adjust to unpredictable environments effectively. However, alongside the positive benefits of softness, these systems face challenges such as low durability, continuous energy demands, and compromised task performance due to the inherently low stiffness of soft materials. These limitations pose significant obstacles to the practical impact of soft engineering systems in the real world beyond innovative concepts. This review presents a strategy that employs materials with variable stiffness to balance adaptability advantages with the challenge of low rigidity. We summarize developments in materials capable of stiffness modulation alongside their applications in electronics, robotics, and biomedical fields. This focus on stiffness modulation at the material unit level is a critical step toward enabling the practical application of soft engineering systems in real‐world scenarios.This article is protected by copyright. All rights reserved.

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Soft Actuators and Robotic Devices for Rehabilitation and Assistance

Pan¹,

Yuan

Liang

et al. 2021

Advanced Intelligent Systems

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Soft actuators and robotic devices have been increasingly applied to the field of rehabilitation and assistance, where safe human and machine interaction is of particular importance. Compared with their widely used rigid counterparts, soft actuators and robotic devices can provide a range of significant advantages; these include safe interaction, a range of complex motions, ease of fabrication, and resilience to a variety of environments. In recent decades, significant effort has been invested in the development of soft rehabilitation and assistive devices for improving a range of medical treatments and quality of life. This review provides an overview of the current state‐of‐the‐art in soft actuators and robotic devices for rehabilitation and assistance, in particular systems that achieve actuation by pneumatic and hydraulic fluid‐power, electrical motors, chemical reactions, and soft active materials such as dielectric elastomers, shape memory alloys, magnetoactive elastomers, liquid crystal elastomers, and piezoelectric materials. Current research on soft rehabilitation and assistive devices is in its infancy, and new device designs and control strategies for improved performance and safe human–machine interaction are identified as particularly untapped areas of research. Finally, insights into future research directions are outlined.

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Wearable Elbow Exoskeleton Actuated with Shape Memory Alloy

Cited by 23 publications

References 4 publications

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

Rigidity‐Tunable Materials for Soft Engineering Systems

Soft Actuators and Robotic Devices for Rehabilitation and Assistance

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