scholarly journals A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees

2020 ◽  
Vol 12 (533) ◽  
pp. eaay2857 ◽  
Author(s):  
Philip P. Vu ◽  
Alex K. Vaskov ◽  
Zachary T. Irwin ◽  
Phillip T. Henning ◽  
Daniel R. Lueders ◽  
...  
Keyword(s):  
Peripheral Nerve ◽  
Real Time ◽  
Upper Limb ◽  
Signal Amplitude ◽  
Real Time Control ◽  
Large Signal ◽  
Time Control ◽  
Peripheral Nerve Interface ◽  
Artificial Hand ◽  
Promising Source

Peripheral nerves provide a promising source of motor control signals for neuroprosthetic devices. Unfortunately, the clinical utility of current peripheral nerve interfaces is limited by signal amplitude and stability. Here, we showed that the regenerative peripheral nerve interface (RPNI) serves as a biologically stable bioamplifier of efferent motor action potentials with long-term stability in upper limb amputees. Ultrasound assessments of RPNIs revealed prominent contractions during phantom finger flexion, confirming functional reinnervation of the RPNIs in two patients. The RPNIs in two additional patients produced electromyography signals with large signal-to-noise ratios. Using these RPNI signals, subjects successfully controlled a hand prosthesis in real-time up to 300 days without control algorithm recalibration. RPNIs show potential in enhancing prosthesis control for people with upper limb loss.

scholarly journals Interactive Real-Time Control System for The Artificial Hand

2020 ◽  
Vol 16 (1) ◽  
pp. 1-10
Author(s):  
Hanadi Jaber ◽  
Mofeed rashid ◽  
Luigi Fortuna
Keyword(s):  
Real Time ◽  
Artificial Limbs ◽  
Svm Classifier ◽  
Average Intensity ◽  
Real Time Control ◽  
Time Control ◽  
Spatial Features ◽  
Density Surface ◽  
Artificial Hand ◽  
Semg Signals

In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.

ElectroMyoGram Pattern Recognition for Real-Time Control of Upper Limb Prosthesis

2016 ◽  
Vol 6 (8) ◽  
pp. 1872-1880 ◽  
Author(s):  
Enas Abdulhay ◽  
Ruba Khnouf ◽  
Abeer Bakeir ◽  
Razan Al-Asasfeh ◽  
Heba Khader
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Real Time Control ◽  
Time Control ◽  
Upper Limb Prosthesis ◽  
Limb Prosthesis

scholarly journals Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang ◽  
Xiao Zhang
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Robotic Arm ◽  
Myoelectric Signal ◽  
Real Time Control ◽  
Myoelectric Signals ◽  
Upper Limb Rehabilitation ◽  
Time Control ◽  
Offline Testing ◽  
Limb Rehabilitation

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.

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

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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