An exoskeletal robot for human shoulder joint motion assist

Kiguchi, Kazuo; Iwami, K.; Yasuda, M.; Watanabe, Keigo; Fukuda, Toshio

doi:10.1109/tmech.2003.809168

Cited by 176 publications

(85 citation statements)

References 18 publications

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“…The human hand has many joints and performs important functions; therefore, injury to or loss of fingers is a serious problem. Prosthetic hands [7][8][9][10][11][12] and robotic devices that assist rehabilitation [13][14][15][16][17][18][19][20][21][22][23][24][25][26] are being developed in many institutes, and some prosthetic hands are available in the world market [7][8][9]. Robotic devices that assist rehabilitation are helpful in achieving an early recovery for injured patients.…”

Section: Introductionmentioning

confidence: 99%

“…Robotic devices that assist rehabilitation are helpful in achieving an early recovery for injured patients. Devices that target the upper limb (shoulder or elbow, without hand) are currently being researched [13][14][15][16][17][18][19], while other work focuses on hand and finger function [20][21][22][23][24][25][26]. While research on prosthetic hardware has achieved a certain level, the next step must focus on how to control these devices.…”

Section: Introductionmentioning

confidence: 99%

“…While research on prosthetic hardware has achieved a certain level, the next step must focus on how to control these devices. One solution is to use sEMG, which in fact has already been applied to controlling a device that assists rehabilitation [19,27] and to estimating the degree of recovery [28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Hioki

Kawasaki

2012

ISRN Rehabilitation

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Background. The surface electromyogram (sEMG) is strongly related to human motion and is useful as a human interface in robotics and rehabilitation. The purpose of this study was to establish a new system for estimating finger joint angles using few sEMG channels. Methods. To deal with a dynamic system, the proposed method adopts time delay factors and a feedback stream into a neural network (NN) with 6 system parameters. The 2 target motion patterns were each tested with 5 subjects. 1000 combinations of system parameter sets were tested. Results. A system with only 4 channels can estimate angles with 7.1-11.8% root mean square (RMS) error, which is approximately the same level of accuracy achieved by other systems using 15 channels. Conclusions. The use of so few channels is a great advantage in an sEMG system because it provides a convenient interface system. This advantage is conferred by the proposed NN system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Hioki

Kawasaki

2012

ISRN Rehabilitation

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“…To the authors' best knowledge, only few works have addressed together the issues listed above. EMG-driven approaches can be classified as: physical-based approaches [24,4] and non-physical-based approaches [26,10,9,7,15]. The physically-based approaches are more realistic as they use musculoskeletal model to estimate muscles forces and joint torques.…”

Section: Introductionmentioning

confidence: 99%

“…The control schema is based on the assisting torque estimation by using a proportional controller as a function of the EMG activation level. An EMG interface to control two degrees of freedom exoskeleton is presented in [10]. This exoskeleton assists flexion/extention and pronation/supination shoulder joint to perform predefined movements.…”

Section: Introductionmentioning

confidence: 99%

Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy

Hassani

Mohammed

2013

Robotics: Science and Systems IX

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Abstract-This paper presents a new approach to control a wearable knee joint exoskeleton driven through the wearer's intention. A realistic bio-inspired musculoskeletal knee joint model is used to control the exoskeleton. This model takes into account changes in muscle length and joint moment arms as well as the dynamics of muscle activation and muscle contraction during lower limb movements. Identification of the model parameters is done through an unconstrained optimization problem formulation. A control law strategy based on the principle of assistance as needed is proposed. This approach guarantees asymptotic stability of the knee joint orthosis and adaptation to human-orthosis interaction. Moreover, the proposed control law is robust with respect to external disturbances. Experimental validations are conducted online on a healthy subject during flexion and extenion of their knee joint. The proposed control strategy has shown satisfactory performances in terms of tracking trajectory and adaptation to human tasks completion.

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A novel hybrid observer‐based model‐free adaptive high‐order terminal sliding mode control for robot manipulators with prescribed performance

Zha,

Wang,

Tian

et al. 2024

Intl J Robust & Nonlinear

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Although widely used in industrial applications, strong nonlinearity and coupling, high computational complexity prevent high precision tracking control of manipulator. In this paper, to overcome the rely on system model and achieve prescribed convergence, a novel hybrid observer‐based model‐free adaptive high‐order fast terminal sliding model control scheme (HO‐MHTSMC) with prescribed performance is proposed for trajectory tracking control of robot manipulators in the existence of friction and external disturbance. The ultra‐local model is used to approximate the original complex system in a model free form in a short sliding time window, which avoid the accurate modeling of the manipulator system. To compensate for the lumped uncertainties, a hybrid observer based on adaptive time‐delay estimation and adaptive second order sliding mode observer (SOSM) is proposed to achieve finite‐time observation and zero estimation error. Besides, a transformation using prescribed performance function is applied to the system to ensure the transient and steady‐state performance of the trajectory tracking in joint space. Furthermore, a high‐order fast terminal sliding mode control algorithm with backstepping control strategy is used to stabilize the whole system and reduce the chattering problem in conventional sliding mode control. The stability analysis of the system is provided by Lyapunov theorem. Finally, numerical study and co‐simulations show that the proposed control scheme has better performance in tracking accuracy and robustness compared with conventional control schemes.

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An exoskeletal robot for human shoulder joint motion assist

Cited by 176 publications

References 18 publications

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy

A novel hybrid observer‐based model‐free adaptive high‐order terminal sliding mode control for robot manipulators with prescribed performance

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