A Motorized Gravity Compensation Mechanism Used for Active Rehabilitation of Upper Limbs

Elk, M.G. van; Driessen, Bart; Dorrepaal, M.; Werff, J.J. van der; Meche, E.G. van der; Aulbers, A.P.

doi:10.1109/icorr.2005.1501073

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…Although, position control in rehabilitation robots could counteract the effect of gravity weight of upper limb, it focused on control accuracy and ignores patients' voluntary participation in task execution, and its effectiveness in rehabilitation therapy needed further improvement. For patients who suffer from muscle weakness, gravity support showed promising results in minimizing the gravity-induced interference for tasks execution (van Elk et al, 2005;Cheng et al, 2015;Runnalls et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

Dubey¹,

Yu²,

Low³

2019

Frontiers Research Topics

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The provision of continuous passive, and intent-based assisted movements for neuromuscular training can be incorporated into a robotic elbow sleeve. The objective of this study is to propose the design and test the functionality of a soft robotic elbow sleeve in assisting flexion and extension of the elbow, both passively and using intent-based motion reinforcement. First, the elbow sleeve was developed, using elastomeric and fabric-based pneumatic actuators, which are soft and lightweight, in order to address issues of non-portability and poor alignment with joints that conventional robotic rehabilitation devices are faced with. Second, the control system was developed to allow for: (i) continuous passive actuation, in which the actuators will be activated in cycles, alternating between flexion and extension; and (ii) an intent-based actuation, in which user intent is detected by surface electromyography (sEMG) sensors attached to the biceps and triceps, and passed through a logic sequence to allow for flexion or extension of the elbow. Using this setup, the elbow sleeve was tested on six healthy subjects to assess the functionality of the device, in terms of the range of motion afforded by the device while in the continuous passive actuation. The results showed that the elbow sleeve is capable of achieving approximately 50% of the full range of motion of the elbow joint among all subjects. Next, further experiments were conducted to test the efficacy of the intent-based actuation on these healthy subjects. The results showed that all subjects were capable of achieving electromyography (EMG) control of the elbow sleeve. These preliminary results show that the elbow sleeve is capable of carrying out continuous passive and intent-based assisted movements. Further investigation of the clinical implementation of the elbow sleeve for the neuromuscular training of neurologically-impaired persons, such as stroke survivors, is needed.

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Section: Introductionmentioning

confidence: 99%

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

Dubey¹,

Yu²,

Low³

2019

Frontiers Research Topics

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Section: Introductionmentioning

confidence: 99%

Assessment of Motor Control during Three-Dimensional Movements Tracking with Position-Varying Gravity Compensation

et al. 2017

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Active movements are important in the rehabilitation training for patients with neurological motor disorders, while weight of upper limb impedes movements due to muscles weakness. The objective of this study is to develop a position-varying gravity compensation strategy for a cable-based rehabilitation robot. The control strategy can estimate real-time gravity torque according to position feedback. Then, the performance of this control strategy was compared with the other two kinds of gravity compensation strategies (i.e., without compensation and with fixed compensation) during movements tracking. Seven healthy subjects were invited to conduct tracking tasks along four different directions (i.e., upward, forward, leftward, and rightward). The performance of movements with different compensation strategies was compared in terms of root mean square error (RMSE) between target and actual moving trajectories, normalized jerk score (NJS), mean velocity ratio (MVR) of main motion direction, and the activation of six muscles. The results showed that there were significant effects in control strategies in all four directions with the RMSE and NJS values in the following order: without compensation > fixed compensation > position-varying compensation and MVR values in the following order: without compensation < fixed compensation < position-varying compensation (p < 0.05). Comparing with movements without compensation in all four directions, the activation of muscles during movements with position-varying compensation showed significant reductions, except the activations of triceps and in forward and leftward movements, the activations of upper trapezius and middle parts of deltoid in upward movements and the activations of posterior parts of deltoid in all four directions (p < 0.05). Therefore, with position-varying gravity compensation, the upper limb cable-based rehabilitation robotic system might assist subjects to perform movements with higher quality and improve the participation of robot-aided rehabilitation training. Further studies are needed to explore the effectiveness and clinic application across pathologies.

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“…Differential rates of recovery occur for various types of impairments. In general, motor functions (mobility, walking, upper limb function, hand function, activities of daily living (ADL)) recover faster than cognitive or language impairments which may recover over 12 months [2].…”

Section: Introductionmentioning

confidence: 99%

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

Peng

Song

et al. 2011

International Journal of Advanced Robotic Systems

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Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

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A Motorized Gravity Compensation Mechanism Used for Active Rehabilitation of Upper Limbs

Cited by 11 publications

References 2 publications

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

Assessment of Motor Control during Three-Dimensional Movements Tracking with Position-Varying Gravity Compensation

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

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