scholarly journals Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5738
Author(s):  
Bridget Schabron ◽  
Jaydip Desai ◽  
Yimesker Yihun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Muscular Dystrophy ◽  
Activities Of Daily Living ◽  
Upper Limb ◽  
Daily Living ◽  
Adaptive Controller ◽  
Robotic Exoskeleton ◽  
Artificial Neural ◽  
Semg Signals

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.

scholarly journals Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

2019 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
Author(s):  
Osama Dorgham ◽  
Ibrahim Al-Mherat ◽  
Jawdat Al-Shaer ◽  
Sulieman Bani-Ahmad ◽  
Stephen Laycock
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Potential ◽  
Eccentric Contraction ◽  
Surface Emg ◽  
Mean Square ◽  
Ann Model ◽  
Semg Signal ◽  
Artificial Neural ◽  
Semg Signals

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

Evaluation of active upper limb ability in cervical spondylotic myelopathy via an artificial neural network

Physiotherapy ◽  
2015 ◽  
Vol 101 ◽  
pp. e1096
Author(s):  
T. Nishi ◽  
K. Fukudome ◽  
K. Yone ◽  
T. Maeda ◽  
K. Hata ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Upper Limb ◽  
Cervical Spondylotic Myelopathy ◽  
Artificial Neural

scholarly journals Predictive Neural Network in Multipurpose Self-Tuning Controller

2020 ◽  
Vol 14 (2) ◽  
pp. 114-120
Author(s):  
Oleksiy Bondar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Adaptive Controller ◽  
Development Environment ◽  
Wide Range ◽  
Artificial Neural ◽  
Arbitrary Precision ◽  
The Right

AbstractA very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.

scholarly journals Adaptive control of two-wheeled mobile balance robot capable to adapt different surfaces using a novel artificial neural network–based real-time switching dynamic controller

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141770089 ◽  
Author(s):  
Ali Unluturk ◽  
Omer Aydogdu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Adaptive Controller ◽  
Linear Displacement ◽  
Real Time Control ◽  
Development Environment ◽  
Dynamic Controller ◽  
Switching Dynamic ◽  
Artificial Neural

In this article, a novel real-time artificial neural network–based adaptable switching dynamic controller is developed and practically implemented. It will be used for real-time control of two-wheeled balance robot which can balance itself upright position on different surfaces. In order to examine the efficiency of the proposed controller, a two-wheeled mobile balance robot is designed and a test platform for experimental setup is made for balance problem on different surfaces. In a developed adaptive controller algorithm which is capable to adapt different surfaces, mean absolute target angle deviation error, mean absolute target displacement deviation error and mean absolute controller output data are employed for surface estimation by using artificial neural network. In a designed two-wheeled mobile balance robot system, robot tilt angle is estimated via Kalman filter from accelerometer and gyroscope sensor signals. Furthermore, a visual robot control interface is developed in C++ software development environment so that robot controller parameters can be changed as desired. In addition, robot balance angle, linear displacement and controller output can be observed online on personal computer. According to the real-time experimental results, the proposed novel type controller gives more effective results than the classic ones.

scholarly journals Engenharia de Reabilitação: Mão Antropomórfica Controlada por Sinais Eletromiográficos

2016 ◽  
Author(s):  
Gerson Jorge S. Suzart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Upper Limb ◽  
Human Hand ◽  
Robotic Device ◽  
Hand Amputation ◽  
Artificial Neural

This article proposes to initiate studies to create a robotic device for people with hand amputation, to a certain degree of the upper limb at the level of the elbow, controlled by muscle stimuli captured by electromyographic sensors. The main objective of this study was to collect the characteristics of the EMG signals for pre stipulated movements, recognizing the patterns of these movements by Artificial Neural Network, to develop a prosthetic model that intentionally involves the anthropomorphism of the hand, with respect to functional, that is, able to move So that if similar or close to the human hand.

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