This study proposes a charge-mode neural stimulator for electrical stimulation systems that utilizes a capacitor-reuse technique with a residual charge detector and achieves active charge balancing simultaneously. The design is mainly used for epilepsy suppression systems to achieve real-time symptom relief during seizures. A charge-mode stimulator is adopted in consideration of the complexity of circuit design, the high voltage tolerance of transistors, and system integration requirements in the future. The residual charge detector allows users to understand the current stimulus situation, enabling them to make optimal adjustments to the stimulation parameters. On the basis of the information on actual stimulation charge, active charge balancing can effectively prevent the accumulation of mismatched charges on electrode impedance. The capacitor- and phase-reuse techniques help realize high integration of the overall stimulator circuit in consideration of the commonality of the use of a capacitor and charging/discharging phase in the stimulation circuit and charge detector. The proposed charge-mode neural stimulator is implemented in a TSMC 0.18 µm 1P6M CMOS process with a core area of 0.2127 mm. Measurement results demonstrate the accuracy of the stimulation's functionality and the programmable stimulus parameters. The effectiveness of the proposed charge-mode neural stimulator for epileptic seizure suppression is verified through animal experiments.
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http://dx.doi.org/10.1109/TBCAS.2024.3380055 | DOI Listing |
This study proposes a charge-mode neural stimulator for electrical stimulation systems that utilizes a capacitor-reuse technique with a residual charge detector and achieves active charge balancing simultaneously. The design is mainly used for epilepsy suppression systems to achieve real-time symptom relief during seizures. A charge-mode stimulator is adopted in consideration of the complexity of circuit design, the high voltage tolerance of transistors, and system integration requirements in the future.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
June 2017
Transparent control is still highly challenging for robotic exoskeletons, especially when a simple strategy is expected for a large-impedance device. To improve the transparency for late-phase rehabilitation when "patient-in-charge" mode is necessary, this paper aims at adaptive identification of human motor intent, and proposed an iterative prediction-compensation motion control scheme for an exoskeleton knee joint. Based on the analysis of human-machine interactive mechanism (HMIM) and the semiphenomenological biomechanical model of muscle, an online adaptive predicting controller is designed using a focused time-delay neural network (FTDNN) with the inputs of electromyography (EMG), position and interactive force, where the activation level of muscle is estimated from EMG using a novel energy kernel method.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
November 2014
Until today it is not entirely clear how humans interact with automated gait rehabilitation devices and how we can, based on that interaction, maximize the effectiveness of these exoskeletons. The goal of this study was to gain knowledge on the human-robot interaction, in terms of kinematics and muscle activity, between a healthy human motor system and a powered knee exoskeleton (i.e.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2007
Institute for Biomedical Technology (BMTI), University of Twente, 7500 EA Enschede, The Netherlands.
This paper introduces a newly developed gait rehabilitation device. The device, called LOPES, combines a freely translatable and 2-D-actuated pelvis segment with a leg exoskeleton containing three actuated rotational joints: two at the hip and one at the knee. The joints are impedance controlled to allow bidirectional mechanical interaction between the robot and the training subject.
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