Upper Limb Flexion Assistance Based on Minimum-Jerk Trajectory Using Wearable Motion-Assist Robot

Enya, Takeshi; Yamane, Michi; Nakamura, Hiroyuki; Aoki, Takaaki; Nishimoto, Yuji; Yano, Ken’ichi

doi:10.3182/20110828-6-it-1002.02055

Cited by 4 publications

(5 citation statements)

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“…From the results presented in these columns, we observed that 45.4% of these studies conducted testing only by numerical simulations, 31.8% performed testing on healthy subjects, and only 9% on patients (Table 3). The tests have been performed on patients in two studies [11,43]. Considering the presented studies, only a few robotic devices for upper limb rehabilitation have been tested in the clinical environment, likely due to the lack of standardized data measurement and evaluation procedure, since verifying their efficacy is a critical issue related to robotic therapy.…”

Section: Methods Applied To the Currently Available Exoskeleton Robotsmentioning

confidence: 99%

“…In addition, these tests were performed on a small number of subjects (maximum of four subjects). Consequently, we report only two kinds of problems encountered during the clinical tests: (i) control problems: test provided low accuracy, which could limit the application of the proposed method to a given domain [11,47]; (ii) generalization problems: low generalization capacity and low accuracy [43].…”

Section: Methods Applied To the Currently Available Exoskeleton Robotsmentioning

confidence: 99%

“…In [25], the spatial trajectory of the end effector was generated on the basis of a minimum-jerk model. A minimum-jerk approach was also adopted in [24,39,43,44]. In [37], Li et al exploited the minimization of potential energy.…”

Section: Approaches Based On Cartesian Motion Planningmentioning

confidence: 99%

“…The need here is control sharing, which is the ability to control the forces applied by the robot [68]. A previously proposed prototype [43] is back-drivable (instrumented compliance). There are two kinds of back-drivability [73], the most advantageous of which, mechanical back-drivability, is strongly dependent on the transmission mechanism of the exoskeleton [73].…”

Section: Multicubic Polynomialinterpolationmentioning

confidence: 99%

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Motion Planning of Upper-Limb Exoskeleton Robots: A Review

2020

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(1) Background: Motion planning is an important part of exoskeleton control that improves the wearer’s safety and comfort. However, its usage introduces the problem of trajectory planning. The objective of trajectory planning is to generate the reference input for the motion-control system. This review explores the methods of trajectory planning for exoskeleton control. In order to reduce the number of surveyed papers, this review focuses on the upper limbs, which require refined three-dimensional motion planning. (2) Methods: A systematic search covering the last 20 years was conducted in Ei Compendex, Inspect-IET, Web of Science, PubMed, ProQuest, and Science-Direct. The search strategy was to use and combine terms “trajectory planning”, “upper limb”, and ”exoskeleton” as high-level keywords. “Trajectory planning” and “motion planning” were also combined with the following keywords: “rehabilitation”, “humanlike motion“, “upper extremity“, “inverse kinematic“, and “learning machine “. (3) Results: A total of 67 relevant papers were discovered. Results were then classified into two main categories of methods to plan trajectory: (i) Approaches based on Cartesian motion planning, and inverse kinematics using polynomial-interpolation or optimization-based methods such as minimum-jerk, minimum-torque-change, and inertia-like models; and (ii) approaches based on “learning by demonstration” using machine-learning techniques such as supervised learning based on neural networks, and learning methods based on hidden Markov models, Gaussian mixture models, and dynamic motion primitives. (4) Conclusions: Various methods have been proposed to plan the trajectories for upper-limb exoskeleton robots, but most of them plan the trajectory offline. The review approach is general and could be extended to lower limbs. Trajectory planning has the advantage of extending the applicability of therapy robots to home usage (assistive exoskeletons); it also makes it possible to mitigate the shortages of medical caregivers and therapists, and therapy costs. In this paper, we also discuss challenges associated with trajectory planning: kinematic redundancy and incompatibility, and the trajectory-optimization problem. Commonly, methods based on the computation of swivel angles and other methods rely on the relationship (e.g., coordinated or synergistic) between the degrees of freedom used to resolve kinematic redundancy for exoskeletons. Moreover, two general solutions, namely, the self-tracing configuration of the joint axis and the alignment-free configuration of the joint axis, which add the appropriate number of extra degrees of freedom to the mechanism, were employed to improve the kinematic incompatibility between human and exoskeleton. Future work will focus on online trajectory planning and optimal control. This will be done because very few online methods were found in the scope of this study.

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Section: Methods Applied To the Currently Available Exoskeleton Robotsmentioning

confidence: 99%

Section: Methods Applied To the Currently Available Exoskeleton Robotsmentioning

confidence: 99%

Section: Approaches Based On Cartesian Motion Planningmentioning

confidence: 99%

Section: Multicubic Polynomialinterpolationmentioning

confidence: 99%

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Motion Planning of Upper-Limb Exoskeleton Robots: A Review

2020

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“…We used an exoskeleton robot that could be worn in daily life. In a previous study, we assessed a wearable robot that assisted upper limb motion for C5-level cervical cord injury patients [2][3] [4]. Here we attempt to obtain the user's motion intention through the residual function of C4 injury patients to support motion.…”

Section: Introductionmentioning

confidence: 99%

Elbow joint motion support for C4 level cervical cord injury patient using an exoskeleton robot

Tominaga

Nakamura

Mizutani

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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We previously proposed a motion support robot and conducted experiments in which a C5-level cervical cord injury patient used the system. This paper proposes to offer motion support to a C4-level spinal cord injury patient who suffers from paralysis of the limbs. Electromyogram (EMG) signals are used to determine the user's intentions. The proposed motion assist robot can then support flexion and extension at the elbow joint. Using frequency analysis of the EMG signal, the patient's intention can be estimated in real time, and the coefficient of viscosity is varied in an admittance controller. With the proposed motion assist method, we were able to realize arbitrary upper limb motion of a C4-level cervical cord injury patient.

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