Robot adaptive behavior to suit patient needs and enable more intensive rehabilitation tasks

Rodriguez–Guerrero, Carlos; Fraile, Juan-Carlos; Pérez-Turiel, Javier

doi:10.1109/icmech.2009.4957230

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The AAN paradigm is based on a robot closed-loop admittance controller [38]. The M, B, and K parameters, which characterize the admittance controller, are updated (increase, decrease, or no change) every second according to the fuzzy logic output.…”

Section: B Adaptive Controlmentioning

confidence: 99%

A Versatile Embedded Platform for Implementation of Biocooperative Control in Upper-Limb Neuromotor Rehabilitation Scenarios

et al. 2023

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Biocooperative control uses both biomechanical and physiological information of the user to achieve a reliable human-robot interaction. In the context of neuromotor rehabilitation, such control can enhance rehabilitation experience and outcomes. However, the high cost and large volume of the commercial systems for physiological signal acquisition are major limitations for the development of such control. We present a highly versatile, low-cost and wearable embedded system that integrates the most commonly used sensors in this field: inertial measurement unit (IMU), electrocardiography (ECG), electromyography (EMG), galvanic skin response (GSR) and skin temperature (SKT) sensors. Additionally, the compact system combines wireless communication for data transmission and a high-efficiency microcontroller for real-time signal processing and control. We tested the system in two common neuromotor rehabilitation scenarios. The first is an upper-limb rehabilitation VR-based exergame, in which the patient must collect as many coins as possible. Movement recognition of the hand and arm is performed based on EMG and IMU information, respectively. The second is adaptive assistive control that adjusts the level of assistance of a wrist rehabilitation robot according to the physiological state and motor performance of the patient using GSR, ECG and SKT data. The quality of the recorded signals and the processing capacity of the system meet the needs of the two upper-limb rehabilitation applications. The wearable system is highly versatile, open, configurable and low cost, and it could promote the development of real-time biocooperative control for a wide range of neuromotor rehabilitation applications.INDEX TERMS Biocooperative control, embedded system, neuromotor rehabilitation, real-time signal processing, wearable sensors.

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Section: B Adaptive Controlmentioning

confidence: 99%

A Versatile Embedded Platform for Implementation of Biocooperative Control in Upper-Limb Neuromotor Rehabilitation Scenarios

et al. 2023

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“…Stroke and spinal cord injury may result in hemiparesis such as limb motion dysfunctions. Fortunately, most of the patients may recover if they are treated with effective rehabilitation programs (Guerrero et al, 2009;Nancy et al, 2003). Conventionally, the rehabilitation program is carried out by physiotherapists who provide hands-on support.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

Jiang

Wang

Zhang

et al. 2015

Industrial Robot: An International Journal

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Purpose – The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN). Design/methodology/approach – The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot. Findings – The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration. Research limitations/implications – This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online. Originality/value – Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

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Using psychophysiological feedback to enhance physical human robot interaction in a cooperative scenario

Rodriguez–Guerrero

Marinero

Pérez-Turiel

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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Human state aware" systems, may be able to improve physical human-robot interaction (pHRI) by adapting their behavior and cooperation level accordingly to human psychophysiological feedback. This paper reviews the impact of the emotions on the motor control performance, and studies the possibility of improving the perceived skill/challenge relation on a multimodal neural rehabilitation scenario. For this purpose, a biocooperative controller that modulate the assistance given by a robotic haptic interface in reaction to undesirable physical and mental states was designed. Open and closed loop experiments made over eleven non impaired subjects, shows that psychophisiological feedback estimates, can help considerably on improving pHRI in a cooperative scenario.

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Robot adaptive behavior to suit patient needs and enable more intensive rehabilitation tasks

Cited by 3 publications

References 15 publications

A Versatile Embedded Platform for Implementation of Biocooperative Control in Upper-Limb Neuromotor Rehabilitation Scenarios

A Versatile Embedded Platform for Implementation of Biocooperative Control in Upper-Limb Neuromotor Rehabilitation Scenarios

Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

Using psychophysiological feedback to enhance physical human robot interaction in a cooperative scenario

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