Electromyography (EMG) Controlled Assistive Rehabilitation System

2014 ◽  
Author(s):  
Robert V. Forshaw ◽  
Nicholas W. Snow ◽  
Jared M. Wolff ◽  
Mansour Zenouzi ◽  
Douglas E. Dow
Keyword(s):  
Electrical Activity ◽  
Motor Function ◽  
Time Window ◽  
Neuromuscular Diseases ◽  
Raspberry Pi ◽  
User Intent ◽  
Triceps Muscle ◽  
Emg Signal ◽  
Rehabilitation System ◽  
Arm Motion

Electromyography (EMG) is a method for monitoring the electrical activity of skeletal muscles. The EMG signal is used to diagnose neuromuscular diseases and muscular injuries. EMG can also be utilized as an indicator of user intent for a muscle contraction for a specific motion. This input signal could be used to control powered exoskeleton prostheses. Limbs with impaired motor function tend to have increased disuse that may result in further muscle weakness. Therapy and other physical activities that increase the use of an impaired limb may contribute to some recovery of motor function. A device that helps to perform activities of daily living may increase usage and enhance recovery. The objective of this project is to make developments toward an EMG controlled assistive rehabilitation system that monitors EMG signals of the bicep and triceps muscles, and drives a motor to assist with arm motion. A motor is used to develop torque that would assist rotations of the arm about the elbow. A pair of EMG sensors (one pair near the biceps and the other near the triceps muscle) transmits electrical activity of the arm to a microcontroller (Raspberry Pi, Raspberry Pi Foundation, United Kingdom). For the prototype, the EMG signal is sampled and rectified within a moving time window to determine the root mean squared (VRMS) value. This value is used by the microcontroller to generate a pulse-width modulated (PWM) signal that controls the motor. Sensors for the motor provide information to an algorithm on the microcontroller. The generated PWM signal is based on the Vrms values for the EMG signal. Testing and analysis has shown a correlation between the EMG Vrms amplitude and muscle generated torque. The EMG controlled assistive rehabilitation system shows promise for assisting motor function for rotations about the elbow. Further algorithmic development is needed to determine the appropriate amount of assistance from the motor for the motor function indicated by user intent.

scholarly journals IOT Enabled Wearable Gloves with SEMG Subsystem with Posture Analysis

2021 ◽  
Vol 9 (9) ◽  
pp. 1690-1695
Author(s):  
Swathi Sangaboina
Keyword(s):  
Electrical Activity ◽  
Real Time ◽  
Muscle Activity ◽  
Patient Treatment ◽  
Raspberry Pi ◽  
Monitoring Device ◽  
Posture Analysis ◽  
Emg Signal ◽  
Remote Server ◽  
Muscle Tissues

Abstract: Electromyogram (EMG) is a technique to track the record , analyze and estimate the electrical activity produced by muscles. This technique is used to detect the muscle issues that harm the nerves activity , muscle tissues and identify the location where they are joined together . This paper discusses the implementation of a project which can be considered as a tool for the acquisition of muscle activity, presentation and real-time attainment of EMG signal using a specific EMG sensor. The live EMG reading is recorded using the Wi-Fi- enabled Raspberrypi and then sent to a remote server in our case ThingSpeak server with the help of IoT concepts which helps in the telemetry of the obtained biomedical signals using the cloud. Results are displayed in ThingSpeak. The live recordings are also obtained on the PC using the serial plotter. This project can also help us in monitor and observe the progress of the patient treatment even if the physiotherapist could not come and data can be directly sent to them. Thus, the project aims to develop an EMG monitoring device based on IoT, for analyzing and acquiring EMG signals. Keywords: EMG sensor, Raspberry pi, LCD, ADS1115

Detection and Conditioning of EMG

2018 ◽  
pp. 961-1000
Author(s):  
İmran Göker
Keyword(s):  
Electrical Activity ◽  
Clinical Studies ◽  
Skeletal Muscles ◽  
Clinical Importance ◽  
Analog To Digital Converter ◽  
Digital Converter ◽  
Analog To Digital ◽  
Emg Signal ◽  
Different Types ◽  
Main Components

In this chapter, the monitoring of the electrical activity of skeletal muscles is depicted. The main components of the detection and conditioning of the EMG signals is explained in the sense of the biomedical instrumentation. But, first, a brief description of EMG generation is introduced. The hardware components of the general instrumentation system used in the acquisition of EMG signal such as amplifier, filters, analog-to-digital converter are discussed in detail. Subsequently, different types of electrodes used in different EMG techniques are mentioned. Then, various EMG signals that can be detected and monitored via EMG systems are described and their clinical importance is discussed with detail. Finally, different EMG techniques used in clinical studies and their purposes are explained with detail.

scholarly journals Online Fault Detection Approach of Unpredictable Inputs: Application to Handwriting System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ines Chihi ◽  
Mohamed Benrejeb
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Mimo System ◽  
Control Strategies ◽  
Control Approach ◽  
User Intent ◽  
Emg Signal ◽  
Detection Approach ◽  
Prosthesis Control ◽  
Detection And Localization

Many investigators are interested in improving the control strategies of hand prosthesis to make it functional and more convenient to use. The most used control approach is based on the forearm muscles activities, named ‘ElectroMyoGraphic’ (EMG) signal. However, these biological signals are very sensitive to many disturbances and are generally unpredictable in time, type, and level. This leads to inaccurate identification of user intent and threatens the prosthesis control reliability. This paper proposed a real-time fault detection and localization approach applied to handwriting device on the plane. This approach allows connecting inputs (IEMG signals)/outputs (pen tip coordinates) data as a parametric model for Multi-Inputs Multi-Outputs (MIMO) system. The proposed approach is considered as a model-independent abrupt or intermittent fault detection method and as an alternative solution to the unpredictable input observer based techniques, without any observability requirements. This approach allows detecting, in real time, several types of faults in one or two inputs signals and in the same or different instants. Our study is appropriate for many rapidly expanding fields and practices, including biomedical engineering, robotics, and biofeedback therapy or even military applications.

scholarly journals Timed motor function tests capacity in healthy children

2015 ◽  
Vol 101 (2) ◽  
pp. 147-151 ◽  
Author(s):  
Aline Chacon Pereira ◽  
Márcia Gonçalves Ribeiro ◽  
Alexandra Prufer de Queiroz Campos Araújo
Keyword(s):  
Motor Function ◽  
Neuromuscular Disorders ◽  
Neuromuscular Diseases ◽  
Vital Signs ◽  
Healthy Children ◽  
Cross Sectional ◽  
Motor Functions ◽  
Function Tests ◽  
Measured Time ◽  
Registration Number

ObjectiveMotor function tests are used clinically and in research in children, particularly in those with neuromuscular disorders. Timed function tests are recommended in the follow-up of patients with neuromuscular disorders. This study was designed to know how healthy children perform on simple timed motor function tests.Material and methodsIn a cross-sectional observational study, 345 children aged 2–12 years, followed at the Federal University of Rio de Janeiro's Institute of Paediatric, were evaluated. To be eligible they had to have acquired independent walking before the age of 14 months, be able to cope and willing to participate in the study. Anthropometric and vital signs were verified, as well as contact with smokers. The following timed motor function tests were measured: time to rise from the floor (TRF), time to walk 10 meters (10MWT) and time to run 10 meters (10MRT).ResultsImprovement in time to perform those motor functions was found to occur in healthy preschool children. Stabilisation of mean times for those motor functions was seen thereafter: TRF of 1.2 s, 10MWT of 10 s and 10MRT of 5 s.ConclusionsWalking and rising speed improve with age in preschoolers, as expected, and is shown to occur up to a plateau level. Our findings for the 10MWT, 10MRT and TRF are in line with those published in 2008 for the 6 minute walk test (6MWT). The motor functions used in the present study require less time and space than the ones in the 6MWT. They should be considered more universally applicable. Those tests could be used in childcare clinics as a screening for motor disorders such as the neuromuscular diseases.Trial registration number1.098.302.

639 Basic Study of an Upper Limb Motor Function Rehabilitation System Using a Haptic Device

2008 ◽  
Vol 2007.20 (0) ◽  
pp. 437-438
Author(s):  
Shoichi SAKAMOTO ◽  
Hokyoo LEE ◽  
Yoshiyuki TAKAHASHI ◽  
Tasuku MIYOSHI ◽  
Tadashi SUZUKI ◽  
...  
Keyword(s):  
Motor Function ◽  
Upper Limb ◽  
Haptic Device ◽  
Basic Study ◽  
Rehabilitation System

The Influence of Motivation for Training by Giving Scores of the Motor Function Using a Rehabilitation System for Upper Limbs

2017 ◽  
Vol 2017 (0) ◽  
pp. 2A2-H03
Author(s):  
Ayaka MORI ◽  
Ken’ichi KOYANAGI ◽  
Yuka MISUMI ◽  
Singo TERAMAE ◽  
Kei SAWAI ◽  
...  
Keyword(s):  
Motor Function ◽  
Upper Limbs ◽  
Rehabilitation System

scholarly journals EMG Signal Analysis of Upper Extremity Motor Function using Balance-handle Device

2016 ◽  
Vol 10 (4) ◽  
pp. 295-303 ◽  
Author(s):  
Choong-Keun Lee ◽  
Ki-Ho Song ◽  
Jae-Yong An ◽  
Sung-Wook Shin ◽  
Sung-Taek Chung
Keyword(s):  
Upper Extremity ◽  
Motor Function ◽  
Signal Analysis ◽  
Emg Signal

Using EMG Signals to Remotely Control a 3D Industrial Robotic Arm

2019 ◽  
Author(s):  
Wafa Batayneh ◽  
Ahmad Bataineh ◽  
Samer Abandeh ◽  
Mohammad Al-Jarrah ◽  
Mohammad Banisaeed ◽  
...  
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Control Device ◽  
Robotic Arm ◽  
Measurement Unit ◽  
Software Interface ◽  
Emg Signal ◽  
Hardware Interface ◽  
Gesture Control ◽  
Arm Motion

Abstract In this paper, a muscle gesture computer Interface (MGCI) system for robot navigation Control employing a commercial wearable MYO gesture Control armband is proposed. the motion and gesture control device from Thalamic Labs. The software interface is developed using LabVIEW and Visual Studio C++. The hardware Interface between the Thalamic lab’s MYO armband and the robotic arm has been implemented using a National Instruments My RIO, which provides real time EMG data needed. This system allows the user to control a three Degrees of freedom robotic arm remotely by his/her Intuitive motion by Combining the real time Electromyography (EMG) signal and inertial measurement unit (IMU) signals. Computer simulations and experiments are developed to evaluate the feasibility of the proposed System. This system will allow a person to wear this/her armband and move his/her hand and the robotic arm will imitate the motion of his/her hand. The armband can pick up the EMG signals of the person’s hand muscles, which is a time varying noisy signal, and then process this MYO EMG signals using LabVIEW and make classification of this signal in order to evaluate the angles which are used as feedback to servo motors needed to move the robotic arm. A simulation study of the system showed very good results. Tests show that the robotic arm can imitates the arm motion at an acceptable rate and with very good accuracy.

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