Patient-driven control of FES-supported standing up and sitting down: experimental results

Riener, Robert; Ferrarin, Maurizio; Pavan, E.; Frigo, C.

doi:10.1109/86.895956

Cited by 80 publications

(54 citation statements)

References 12 publications

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“…In this way, the artificial robot controller is integrated into the control actions of the intact neuromuscular system of the subject. Similar to approaches proposed in [5] and [10], the hand and foot support forces are used to characterize the subject's volition and are thus used as feedback to the controller. We named the approach "Patient-Driven Robot-Assisted Motion Augmentation" (PDRAMA).…”

Section: Human Voluntary Activity Integration In the Control Of Amentioning

confidence: 99%

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Rehabilitation Robot Cell for Multimodal Standing-Up Motion Augmentation

Kamnik

Bajd

Williamson

et al.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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Abstract-The paper presents a robot cell for multimodal standing-up motion augmentation. The robot cell is aimed at augmenting the standing-up capabilities of impaired or paraplegic subjects. The setup incorporates the rehabilitation robot device, functional electrical stimulation system, measurement instrumentation and cognitive feedback system. For controlling the standing-up process a novel approach was developed integrating the voluntary activity of a person in the control scheme of the rehabilitation robot. The simulation results demonstrate the possibility of "patient-driven" robotassisted standing-up training. Moreover, to extend the system capabilities, the audio cognitive feedback is aimed to guide the subject throughout rising. For the feedback generation a granular synthesis method is utilized displaying highdimensional, dynamic data. The principle of operation and example sonification in standing-up are presented. In this manner, by integrating the cognitive feedback and "patientdriven" actuation systems, an effective motion augmentation system is proposed in which the motion coordination is under the voluntary control of the user.

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Section: Human Voluntary Activity Integration In the Control Of Amentioning

confidence: 99%

“…Training of standing-up is usually performed by the manual support of physiotherapists using also different mechanical aids. The back sled and seesaw construction are typical examples [4], [5]. Mechanical aids are usually constructed as counterweight-based passive devices intended to aid the subject and assure his stability.…”

Section: Introductionmentioning

confidence: 99%

Rehabilitation Robot Cell for Multimodal Standing-Up Motion Augmentation

Kamnik

Bajd

Williamson

et al.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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“…Thus, few papers address biomechanical parameters identification in FES context, and they used macroscopic model for global force production [15]. Consequently, we described an approach for coupling the model with in-vivo measurements, that is, using a multiscale muscle model in an estimation procedure in order to perform the identification of the parameters, hence, giving access to physiologically meaningful parameters of the muscle.…”

Section: In Vivo Identification For Subject-specific Parametersmentioning

confidence: 99%

In Vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF

Hayashibe

Guiraud

Poignet

2013

ISRN Rehabilitation

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Skeletal muscle system has nonlinear dynamics and subject-specific characteristics. Thus, it is essential to identify the unknown parameters from noisy biomedical signals to improve the modeling accuracy in neuroprosthetic control. The objective of this work is to develop an experimental identification method for subject-specific biomechanical parameters of a physiological muscle model which can be employed to predict the nonlinear force properties of stimulated muscle. Our previously proposed muscle model, which can describe multiscale physiological system based on the Hill and Huxley models, was used for the identification. The identification protocols were performed on two rabbit experiments, where the medial gastrocnemius was attached to a motorized lever system to record the force by the nerve stimulation. The muscle model was identified using nonlinear Kalman filters: sigmapoint and extended Kalman filter. The identified model was evaluated by comparison with experimental measurements in the cross-validation manner. The feasibility could be demonstrated by comparison between the estimated parameter and the measured value. The estimates with SPKF showed 5.7% and 2.9% error in each experiment with 7 different initial conditions. It reveals that SPKF has great advantage especially for the identification of multiscale muscle model which accounts for the high nonlinearity and discontinuous states between muscle contraction and relaxation process.

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“…In both proposals, the stimulation sequences were determined on the basis of subject's activity, i.e., known subject body position and arm reactions. The approaches have been evaluated experimentally and proved efficient in providing the "patient-driven" nature of the stand-up [13], [14]. However, the difficulty in closed-loop stimulation control is that the information fed back to the controller is supposed to be provided by the sensors, normally goniometers and accelerometers, attached to the subject's body.…”

mentioning

confidence: 99%