Real-time fuzzy trajectory generation for robotic rehabilitation therapy

Martin, Peter; Emami, M. Reza

doi:10.1109/icorr.2009.5209533

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…To complete the fuzzy inference system, the Takagi-Sugeno-Kang (TSK) inference method is integrated into the system to determine the corresponding crisp outputs for each input set. This unique approach to the generation of fuzzy rules for compliant trajectory generation was proposed in [9] and is shown in Fig. 1 (Block A).…”

Section: B Compliant Trajectory Generatormentioning

confidence: 99%

Neuro-fuzzy compliance control for rehabilitation robotics

Martin

Emami

2010

2010 3rd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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This paper presents a methodology for the design of a real-time neuro-fuzzy controller for the robotic rehabilitation of patients with upper-limb dysfunction due to neurological diseases. The approach utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. It also allows for the fine-tuning of system dynamics using linguistic variables. The rule base for the system is trained using a fuzzy clustering algorithm and applied to experimental data gathered during traditional therapy sessions. The compliance rule base is combined with a hybrid neuro-fuzzy compensator to automatically tune the dynamics of the system on-line. The control algorithm is implemented as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations. Preliminary experimentation has shown promising results indicating that the proposed methodology can accurately replicate the desired compliance profiles in real-time.

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Section: B Compliant Trajectory Generatormentioning

confidence: 99%

Neuro-fuzzy compliance control for rehabilitation robotics

Martin

Emami

2010

2010 3rd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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“…During robot-assisted rehabilitation, this path is altered using compliance or impedance control strategies that are developed using interaction forces and position errors [17,24,27]. Robot path generation from the recordings of subject-specific and trainer-induced leg trajectories is proposed in [28,29]. On the other hand, the further advanced path generation strategy proposed by Vallery et al [30] exploits the learning of healthy limb movements in deciding the commanded robot path for hemiplegic patients.…”

Section: Takedownmentioning

confidence: 99%

“…Desired or reference paths for rehabilitation robots are normally well-defined paths that are encountered during ADLs. During therapeutic treatments, these motion paths require altering after acquiring more information about the patient's abilities from their interaction with robots and subsequent measurements [29]. This information can also be obtained from the movements of healthy limbs in hemiplegic patients [30].…”

Section: B Rehabilitation Path Generationmentioning

confidence: 99%

Musculoskeletal Model for Path Generation and Modification of an Ankle Rehabilitation Robot

Jamwal

Hussain

Tsoi³

et al. 2020

IEEE Trans. Human-Mach. Syst.

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While newer designs and control approaches are being proposed for rehabilitation robots, vital information from the human musculoskeletal system should also be considered. Incorporating knowledge about joint biomechanics during the development of robot controllers can enhance the safety and performance of robot-aided treatments. In the present work, the optimal path or trajectories of a parallel ankle rehabilitation robot were generated by minimizing joint reaction moments and the tension along ligaments and muscle-tendon units. The simulations showed that using optimized robot paths, user efforts could be reduced to 80%, thereby ensuring less strain on weaker or stiffer ligaments, etc. Additionally, to limit the moments applied by the robot in stiff or constrained directions, the intended robot path was modified to move the commanded position in the direction opposite to that of the position error. Such online modification of the robot path can lead to a reduction in forces applied by a robot to the subject. Simulation results and experimental findings with healthy subjects using an ankle rehabilitation robot prototype and subsequent statistical analysis further validated that path modification based on ankle joint biomechanics results in a reduction in undesired forces experienced by human users during treatment. Index Terms-ankle joint musculoskeletal modeling, optimization of muscle forces, parallel ankle rehabilitation robot, robot path generation and modification I.

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“…The following devices are not included in this review: platformbased robotic devices, e.g. Rutgers Ankle (31), which require the patient to be in a seated position (32,33); robotic devices utilizing functional electrical stimulation (FES) (34,35); and passive orthoses with no mechanical power or actuation (36). The preliminary design and evaluation of robotic orthoses published in the form of conference proceedings is also not included in this review.…”

Section: Introductionmentioning

confidence: 99%

Single joint robotic orthoses for gait rehabilitation: An educational technical review

Hussain¹,

Jamwal²,

Ghayesh³

2016

J Rehabil Med

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Robot-assisted physical gait therapy is gaining recognition among the rehabilitation engineering community. Several robotic orthoses for the treatment of gait impairments have been developed during the last 2 decades, many of which are designed to provide physical therapy to a single joint of the lower limb; these are reviewed here. The mechanism design and actuation concepts for these single joint robotic orthoses are discussed. The control algorithms developed for these robotic orthoses, which include trajectory tracking control and assist-as-needed control, are described. Finally, the mechanism design and control of single joint robotic orthoses are discussed. There is a strong need to develop assist-as-needed control algorithms and to perform clinical evaluation of these robotic orthoses in order to establish their therapeutic efficacy.

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Real-time fuzzy trajectory generation for robotic rehabilitation therapy

Cited by 6 publications

References 41 publications

Neuro-fuzzy compliance control for rehabilitation robotics

Neuro-fuzzy compliance control for rehabilitation robotics

Musculoskeletal Model for Path Generation and Modification of an Ankle Rehabilitation Robot

Single joint robotic orthoses for gait rehabilitation: An educational technical review

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