Sensors and Actuation Technologies in Exoskeletons: A Review

Tiboni, Monica; Borboni, Alberto; Vérité, Fabien; Bregoli, Chiara; Amici, Cinzia

doi:10.3390/s22030884

Cited by 56 publications

(22 citation statements)

References 249 publications

(415 reference statements)

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“…Therefore, making a system that will allow the user to "feel" the subtle texture details on an object's surface is not available. A recent review of exoskeleton actuation technology can be found in Tiboni et al [15].…”

Section: Simulators Using Haptic Exoskeleton Glovesmentioning

confidence: 99%

Improving Medical Simulation Using Virtual Reality Augmented by Haptic Proxy

Boulanger¹,

Wang²,

Hanzaki³

2023

Modern Development and Challenges in Virtual Reality

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This chapter explores how the realism of haptic perception in virtual reality can be significantly enhanced with the help of the concept of haptic proxy. In haptic proxy, the position and orientation of physical objects are tracked in real-time and registered to their virtual counterparts. A compelling sense of tactile immersion can be achieved if the tracked objects have similar tactile properties to their virtual counterpart. A haptic proxy prototype was developed, and a pilot study was conducted to determine if the haptic proxy system is more credible than standard virtual reality. To test our prototype, we performed simple medical tasks such as moving a patient’s arm and aiming a syringe to specific locations. Our results suggest that simulation using a haptic proxy system is more believable and user-friendly and can be extended to developing new generations of open surgery simulators.

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Section: Simulators Using Haptic Exoskeleton Glovesmentioning

confidence: 99%

Improving Medical Simulation Using Virtual Reality Augmented by Haptic Proxy

Boulanger¹,

Wang²,

Hanzaki³

2023

Modern Development and Challenges in Virtual Reality

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“…Alongside these are active systems that use an external energy source, for example, to adjust the level and course of support during movement to match the user’s activity. Sorted in descending order of frequency, electric motors (e.g., brushed or brushless DC motors), pneumatic actuators (e.g., pneumatic artificial muscles), and to some extent, even hydraulic actuators are used ( Tiboni et al, 2022 ). In general, active systems can adjust the support characteristics in the application through active control and the implemented sensor technology.…”

Section: Fundamentals For Developing the Frameworkmentioning

confidence: 99%

“…In general, active systems can adjust the support characteristics in the application through active control and the implemented sensor technology. Depending on the controlled variable, dynamic sensors [e.g., pressure, torque, force sensors, an inertial measurement unit (IMU)], encoders, and electromyographic (EMG) sensors are used to measure physical quantities such as forces, torques, acceleration, position, displacement, and muscle activities ( Tiboni et al, 2022 ). The hybrid approach of semi-active systems represents another category.…”

Section: Fundamentals For Developing the Frameworkmentioning

confidence: 99%

Framework for qualifying exoskeletons as adaptive support technology

2022

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The fifth industrial revolution and the accompanying influences of digitalization are presenting enterprises with significant challenges. Regardless of the trend, however, humans will remain a central resource in future factories and will continue to be required to perform manual tasks. Against the backdrop of, e.g., societal and demographic changes and skills shortage, future-oriented support technologies such as exoskeletons represent a promising opportunity to support workers. Accordingly, the increasing interconnection of human operators, devices, and the environment, especially in human-centered work processes, requires improved human-machine interaction and further qualification of support systems to smart devices. In order to meet these requirements and enable exoskeletons as a future-proof technology, this article presents a framework for the future-oriented qualification of exoskeletons, which reveals potential in terms of user-individual and context-dependent adaptivity of support systems. In this context, a framework has been developed, allowing different support situations to be classified based on elementary functions. Using these support function dependencies and characteristics, it becomes possible to describe adaptive system behavior for human-centered support systems such as exoskeletons as a central aspect. For practical illustration, it is shown for an exemplary active exoskeleton using the example of user-individuality and context-specificity how the support characteristics of exoskeletons in the form of different support characteristics can bring about a purposeful and needs-based application for users and can contribute valuably to design future workplaces.

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“…[3]- [5]. The research on exoskeletons for human performance augmentation dates from the 1960s and significant progresses have been made in corresponding aspects in recent two decades, for example, mechanical design [6], sensors [7], actuators [8], human motion intention recognition [9], gait phase detection [10], [11], motion tracking [12], and so on. Control strategies receive much more attention of the researchers.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

Zheng

Liu

et al. 2023

IEEE Access

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The lower-limb exoskeleton for human performance augmentation (LEHPA) in sensitivity amplification control (SAC) is vulnerable to model parameter uncertainties and unmodeled dynamics due to its large sensitivity to external disturbances. This paper proposes sensitivity adaptation based on deep reinforcement learning (SADRL) to reduce the dependence on the model accuracy and tackle the ever-changing human-exoskeleton interaction (HEI) dynamics by interpreting the sensitivity adjustment as a Markov Decision Process (MDP). The agent learns an appropriate sensitivity for each exoskeleton state. To train the control policy safely and efficiently, a multibody simulation environment is created to implement the training process, accompanied by a novel hybrid inverse-forward dynamics simulation method to carry out the simulation. For comparison purposes, the SAC controller is introduced as a benchmark. A novel performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to evaluate the control effect of the trained SADRL controller quantitatively. The SADRL controller is compared with the SAC controller at five specified walking speeds, resulting in a lumped HEI force ratio of 0.54. The total decrease of HEI forces demonstrates the superior control effect of the SADRL strategy.

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Sensors and Actuation Technologies in Exoskeletons: A Review

Cited by 56 publications

References 249 publications

Improving Medical Simulation Using Virtual Reality Augmented by Haptic Proxy

Improving Medical Simulation Using Virtual Reality Augmented by Haptic Proxy

Framework for qualifying exoskeletons as adaptive support technology

Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

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