Artificial and natural sensors in FES-assisted human movement control

Veltink, Petrus H.; Sinkjær, Thomas; Baten, Christian T.M.; Bergveld, Piet; Spek, J. van der; Haugland, Morten Kristian

doi:10.1109/iembs.1998.744684

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…The activation and the relaxation of the muscle cannot be simultaneous For it, there is exists always a time delay. In this case, the Ca² dynamics is defined as follows [48]:…”

Section: Withmentioning

confidence: 99%

Feedback Error Learning with Sliding Mode Control for Functional Electrical Stimulation: Elbow Joint Simulation

Barbouch*¹,

N²,

Belghith³

et al. 2019

IJITEE

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Motivation: Upper-limb motor impairment is one of the most common consequences after Stroke. Limited capability for performing reaching and grasping movements hinders the execution of most activities of daily living. Consequently, the quality lives of the affected individuals are severely compromised. Due to these facts, the recovery of the upper limb functional capabilities is currently one of the keystones of the rehabilitation therapy. Background: Researchers are developing new methods and technologies to boost the outcomes of rehabilitation therapy. A hybrid robotic system has been proposed as a promising rehabilitation technology that combines a passive device (Armeo Spring exoskeleton) to support the arm weight against gravity with a Functional Electrical Stimulation (FES) system to execute the reaching task. This system provides to patients the possibility of training specifically and intensive exercises. Objective: The main objective of this paper is to investigate the performance and robustness of a Feedback Error learning (FEL) scheme mixed with sliding mode control (SMC) to control the FES. Methods: We implemented a nonlinear model describing the muscle response to FES and the dynamic behavior of the elbow joint. Using this model we carried out a simulation study to compare four control strategies: computed torque control (CTC), sliding mode Control (SMC), and adaptive feedback control using FEL: ANN+ CTC and FEL: ANN+SMC. We tested these controllers in two different simulation conditions: In the absence and presence of fatigue. To check the performance of the controllers, we compared the root means square (RMSE) of tracking error and the Normalized RMS of muscle stimulation for various range of movement (ROM). Results: All four controllers achieved good tracking performance in the absence of perturbations. When introducing muscle fatigue, good tracking performance is given essentially by the adaptive control ANN+SMC. Conclusion: Among the proposed approaches, we conclude that the adaptive control (FEL: ANN + SMC) is the most efficient and robust controller, which has been proven by calculating RMSE.

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“…The activation and the relaxation of the muscle cannot be simultaneous For it, there is exists always a time delay. In this case, the Ca² dynamics is defined as follows [48]:…”

Section: Withmentioning

confidence: 99%

Feedback Error Learning with Sliding Mode Control for Functional Electrical Stimulation: Elbow Joint Simulation

Barbouch*¹,

N²,

Belghith³

et al. 2019

IJITEE

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“…Accelerometers, rate gyroscopes and goniometers measure human body movements (Veltink et al, 1998;Luinge et al, 1999;Veltink, 1999). Certain movement sensors, like capacitive accelerometers (Lötters et al, 1995) and hall-effect goniometers (Johnson et al, 1999) are potentially implantable.…”

Section: Information Exchange Of Mechanical Quantities and Mechanicalmentioning

confidence: 99%

Biomechatronics – Assisting the Impaired Motor System

Veltink

Koopman

Helm

et al. 2001

Archives of Physiology and Biochemistry

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Biomechatronics concerns the interdisciplinary field of interaction with the human neuromuscular-skeletal system with the objective to assist impaired human motor control. In this field technology is developed that integrates neuroscience, robotics, interface and sensor technology, dynamic systems and control theory. The primary issue in this field concerns the concepts of assisting impaired human motor function. The secondary, derived, issue concerns possible methods of interfacing with the human body at all hierarchical levels of the human motor system. The application of motor assist systems may serve several goals: it can take over part of the affected motor control, enable the physiological motor system to perform the desired function or aid in training the impaired physiological system. The progress in these issues are reviewed and their potential implications for assistance of the impaired human motor system are discussed.

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“…Therefore, a well-developed calibration process is essential for the development of sensing fabrics in posture and movement measurements. Fahrenberg et al (1997);Foerster et al (1999); Morris (1973); Veltink et al (1993Veltink et al ( , 1998; Wu and Ladin (1999). .…”

Section: Difficulties On Extraction Of Signalmentioning

confidence: 99%

Clinical Applications of Sensors for Human Posture and Movement Analysis

Wong¹,

Wong²,

Lo³

2007

Prosthetics &Amp; Orthotics International

162

102

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Measurement of human posture and movement is an important area of research in the bioengineering and rehabilitation fields. Various attempts have been initiated for different clinical application goals, such as diagnosis of pathological posture and movements, assessment of pre-and post-treatment efficacy and comparison of different treatment protocols. Image-based methods for measurements of human posture and movements have been developed, such as the photogrammetry, optoelectric technique and video analysis. However, it is found that these methods are complicated to set up, time-consuming to operate and could only be applied in laboratory environments. Electronic sensors and systems with advanced technology, namely accelerometer, gyroscope, flexible angular sensor, electromagnetic tracking system and sensing fabrics, have been developed and applied to solve the relevant application problems of the image-based methods. Nonetheless, other problems for using these electronic sensors emerged, including the environment influence and signal extraction difficulties. Further development of these electronic sensors and measurement methods could enhance their clinical applications in institutional as well as community levels. This article reviews the possible applications of these electronic sensors and systems, and precautions of their applications in analysis of human posture and movement. Such information would help researchers and clinicians in selecting and developing the most appropriate measurement techniques of using the electronic sensors for clinical applications of human posture and movement analysis.

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Artificial and natural sensors in FES-assisted human movement control

Cited by 7 publications

References 17 publications

Feedback Error Learning with Sliding Mode Control for Functional Electrical Stimulation: Elbow Joint Simulation

Feedback Error Learning with Sliding Mode Control for Functional Electrical Stimulation: Elbow Joint Simulation

Biomechatronics – Assisting the Impaired Motor System

Clinical Applications of Sensors for Human Posture and Movement Analysis

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