Real-time control of a robotic arm by neuronal ensembles

Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

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Real Time Control of Robotic Arm Using Electromyogram (EMG) Signals

International Journal of Science and Research (IJSR) ◽

10.21275/v5i1.nov152879 ◽

2016 ◽

Vol 5 (1) ◽

pp. 797-800

Keyword(s):

Real Time ◽

Robotic Arm ◽

Real Time Control ◽

Time Control

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Real-Time Control System for Improved Precision and Throughput in an Ultrafast Carbon Fiber Placement Robot Using a SoC FPGA Extended Processing Platform

International Journal of Reconfigurable Computing ◽

10.1155/2017/3298734 ◽

2017 ◽

Vol 2017 ◽

pp. 1-20

Author(s):

Gilberto Ochoa-Ruiz ◽

Romain Bevan ◽

Florent de Lamotte ◽

Jean-Philippe Diguet ◽

Cheng-Cong Bao

Keyword(s):

Real Time ◽

Robotic Arm ◽

Real Time Control ◽

Maximum Throughput ◽

Original Solution ◽

Real Time Operating System ◽

Fiber Placement ◽

Time Control ◽

Retrieval Mechanism ◽

Processing Platform

We present an architecture for accelerating the processing and execution of control commands in an ultrafast fiber placement robot. The system consists of a robotic arm designed by Coriolis Composites whose purpose is to move along a surface, on which composite fibers are deposed, via an independently controlled head. In first system implementation, the control commands were sent via Profibus by a PLC, limiting the reaction time and thus the precision of the fiber placement and the maximum throughput. Therefore, a custom real-time solution was imperative in order to ameliorate the performance and to meet the stringent requirements of the target industry (avionics, aeronautical systems). The solution presented in this paper is based on the use of a SoC FPGA processing platform running a real-time operating system (FreeRTOS), which has enabled an improved comamnd retrieval mechanism. The system’s placement precision was improved by a factor of 20 (from 1 mm to 0.05 mm), while the maximum achievable throughput was 1 m/s, compared to the average 30 cm/s provided by the original solution, enabling fabricating more complex and larger pieces in a significant fraction of the time.

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Robotic Arm Control System Based on Brain-Muscle Mixed Signals

10.21203/rs.3.rs-528958/v1 ◽

2021 ◽

Author(s):

Li-Wei Cheng ◽

Duan-Ling Li ◽

Gong-Jing Yu ◽

Zhong-Hai Zhang ◽

Shu-Yue Yu

Keyword(s):

Control System ◽

Real Time ◽

Feature Recognition ◽

Robotic Arm ◽

Real Time Control ◽

Existing Problems ◽

Time Control ◽

Emg Signal ◽

Extended Application ◽

Robotic Arm Control

Abstract Aiming at the existing problems of BCI (brain computer interface), such as single input signal source, low accuracy of feature recognition, and less output control instructions, this paper proposes a robotic arm control system based on EEG (electroencephalogram) and EMG (electromyogram) mixed signals. The system flow is as follows: Firstly, the EMG signal of the unilateral arm and the EEG signal of the left and right hand motor imagery is collected synchronously. Then the collected EEG and EMG signals are extracted and classified, and the corresponding classification instructions are obtained. Finally, the multi-instruction real-time control of the robotic arm is realized under the classification instruction. The experimental verification results show that: The 10 subjects all realized the real-time multi-command control of the robotic arm, and the average recognition accuracy of each action reached more than 94%. The proposed system enriches the diversity of hybrid BCI and provides a theoretical basis and application foundation for the extended application of BCI in robotic arm control.

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A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

Methods of Information in Medicine ◽

10.1055/s-0038-1634619 ◽

1995 ◽

Vol 34 (05) ◽

pp. 475-488

Author(s):

B. Seroussi ◽

J. F. Boisvieux ◽

V. Morice

Keyword(s):

Real Time ◽

Linear Time ◽

Treatment Plan ◽

Control Architecture ◽

Real Time Control ◽

Time Representation ◽

Time Control ◽

Continuous Support ◽

Definition Of ◽

Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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