MOVEMENT OF PROSTHETIC ARM USING MACHINE LEARNING TECHNOLOGY

Electromyography (EMG) is a strategy utilized for recording electrical exercises delivered by utilizing skeletal muscle tissues having various helpful realities about the muscle(s) condition. The solid machine is responsible for uncommon activities related to the human body. The bio-signals recorded

Project Title

MOVEMENT OF PROSTHETIC ARM USING MACHINE LEARNING TECHNOLOGY

Project Area of Specialization

Artificial Intelligence

Project Summary

Electromyography (EMG) is a strategy utilized for recording electrical exercises delivered by utilizing skeletal muscle tissues having various helpful realities about the muscle(s) condition. The solid machine is responsible for uncommon activities related to the human body. The bio-signals recorded are known as myoelectric or electromyogram. The example acknowledgment machine dependent on EMG has been widely utilized in a few parts of biomedical applications, i.E. clinical finding, restoration robots, assistive PCs, upper-appendage and lower-appendage prosthesis, wearable gadgets and fueled exoskeletons. EMG alarms can be gotten either by means of utilizing concentric needle cathodes or floor anodes. Since the sEMG signal is deterministic and stochastic in nature so it is prescribed never again to utilize it straightforwardly in prosthetic gadgets. Hamstring and quadriceps bulk is the agonistic muscle tissue in knee flexion and expansion moves separately. The advancement of mechanized frameworks that help to analyze neuromuscular clutters will be permitted by amassing the insights all through such assessments that may likewise bring about an effective skill of the character of such issues. These modernized analytic structures can give help to clinical specialists in identifying irregularities inside the neuromuscular machine. We proposed a contraption that will naturally characterize the abnormalities of the hamstring and quadriceps solid tissues worried in knee joint developments. For the location of abnormalities in muscle exercises different sign class systems have been proposed in. Example type-based thoroughly control frameworks separate an immovable of time, recurrence and time-recurrence area works that depict the sEMG makes a decent path aware of catch different sorts of movement. Highlights extricated from sEMG alarms give enough measurements, which have been utilized to characterize the sEMG cautions. An examination view has been done in, reasoned that the choice of capacities impacts outstandingly on the general execution of classification than the selection of classifiers. Because of the innovative headways in pc equipment and programming, the preparation of complex framework learning calculations on colossal datasets will get quick and simple. From the sEMG signal which changes as a quality of time, abilities might be determined inside the type of numeric qualities. Time-space capacities had been boundlessly utilized in sEMG signal class since they're exceptionally easy to ascertain, simple to utilize and no significant change is required. The classification precision of the equivalent class calculation without the need to develop the preparing power. Right now, Mean Absolute Value (MAV), Zero Crossing, Waveform Length (WL), Slope Sign Change had been utilized as the calculation for the class of sEMG signals. 

Project Objectives

• The purpose of this study to make an existing prosthetic arm moves in different directions/positions using machine learning techniques.
• To investigate the comparative analysis and report the accuracy.
• Compared with other existing methods.

Project Implementation Method

1. EMG Record

2. Pre Processing

3.Post-Processing

4. Feature Extraction 

5. Feature Classification

Benefits of the Project

• To perform the multiple degrees of freedom of prosthetic arm and to increase the speed of processing.

Technical Details of Final Deliverable

• The project is designed only for the lower limbs using four muscle movements. However, hip movements and any other muscle movements are not considered as a part of this project.
• The system utilizes non-invasive surface electrodes to collect the data from sEMG signals for the muscle movements.
• For the design and development of the intelligent system, MATLAB R2019a is used

Final Deliverable of the Project

Hardware System

Core Industry

Health

Other Industries

Manufacturing

Core Technology

Artificial Intelligence(AI)

Other Technologies

Robotics, Wearables and Implantables

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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