An implementation of movement classification for prosthesis control using custom-made EMG system

Mejic, Luka; Došen, Strahinja; Ilić, Vojin; Stanisic, Darko; Jorgovanović, Nikola

doi:10.2298/sjee1701013m

Cited by 4 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time-domain feature extraction is the most common method because these features provide high recognition rate and low computational cost. Root mean square (RMS) [ 30 , 31 , 32 , 33 ] and mean absolute value (MAV) [ 29 , 33 , 34 ] are most popular among these features. Time-frequency domain features which characterize varying frequency information at different time locations have received attention recently.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Sun

Zhang

Zhao

et al. 2018

Sensors

View full text Add to dashboard Cite

The novel human-computer interface (HCI) using bioelectrical signals as input is a valuable tool to improve the lives of people with disabilities. In this paper, surface electromyography (sEMG) signals induced by four classes of wrist movements were acquired from four sites on the lower arm with our designed system. Forty-two features were extracted from the time, frequency and time-frequency domains. Optimal channels were determined from single-channel classification performance rank. The optimal-feature selection was according to a modified entropy criteria (EC) and Fisher discrimination (FD) criteria. The feature selection results were evaluated by four different classifiers, and compared with other conventional feature subsets. In online tests, the wearable system acquired real-time sEMG signals. The selected features and trained classifier model were used to control a telecar through four different paradigms in a designed environment with simple obstacles. Performance was evaluated based on travel time (TT) and recognition rate (RR). The results of hardware evaluation verified the feasibility of our acquisition systems, and ensured signal quality. Single-channel analysis results indicated that the channel located on the extensor carpi ulnaris (ECU) performed best with mean classification accuracy of 97.45% for all movement’s pairs. Channels placed on ECU and the extensor carpi radialis (ECR) were selected according to the accuracy rank. Experimental results showed that the proposed FD method was better than other feature selection methods and single-type features. The combination of FD and random forest (RF) performed best in offline analysis, with 96.77% multi-class RR. Online results illustrated that the state-machine paradigm with a 125 ms window had the highest maneuverability and was closest to real-life control. Subjects could accomplish online sessions by three sEMG-based paradigms, with average times of 46.02, 49.06 and 48.08 s, respectively. These experiments validate the feasibility of proposed real-time wearable HCI system and algorithms, providing a potential assistive device interface for persons with disabilities.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Sun

Zhang

Zhao

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Three methods of computing the maximum values of muscle activation force were prepared. The first one consists in computation the sums of waveform lengths [3]. This method is expressed with the formula below (1), where x(k) indicates a given sample, x(k − 1) is a previous sample and N is a number of all samples in a given window: …”

Section: Methodsmentioning

confidence: 99%

“…There are many professional devices which were developed for specific scientific studies, e.g. those concerning electromyographically controlled prostheses [3] or the development of a system for classification of gestures with the use of a 3D accelerometer [4]. Also, a small portable wireless prototype of a system using EMG electrodes was developed for the electrostimulation therapy of patients with motor paralysis [5].…”

Section: Introductionmentioning

confidence: 99%

Applying EMG technology in medial and lateral elbow enthesopathy treatment using Myo motion controller

Grabczyński

Szklanny

Wrzeciono

2019

Australas Phys Eng Sci Med

View full text Add to dashboard Cite

Electromyography (EMG) is a diagnostic technique allowing for the detection of signals generated by changes in electrical potentials of striated muscles. The application of this technology is becoming an increasingly popular subject of scientific research. With the appearance of new devices retrieving EMG data, novel methods of its processing for various purposes are being developed. One such device is the Myo movement controller, produced by Thalmic Labs (now North). The device has been used for the analysis of muscle activation levels in patients with "tennis elbow" and "golfer’s elbow"—conditions of upper limbs which usually result from occupational injuries. The process of their rehabilitation is complex and requires a continuous monitoring of its progress. The data obtained by means of the Myo controller was used for pattern recognition of an injured hand with relation to the healthy one. The study involved examining ten subjects, including five controls. The results indicate that the muscle activation force is considerably lower in injured individuals. The arithmetic mean for the 6 analyzed motions in the injured group is 38.54% lower. The SmartEMG application ( https://www.smartemg.com ) enables the implementation of procedures performed during an examination as well as those involved in the management of the collected recordings. The study produced satisfactory results, which indicates the possibility of using the Myo controller in the treatment of elbow enthesopathy.

show abstract

“…When using a prosthesis, the covering tissues of the stump experience stresses [1][2][3], which are the main cause of inconvenience, pain and injury, reducing the positive effect of rehabilitation [4][5][6][7]. The anatomical and functional features of the forearm stump are characterized by a significant number of bone protrusions, covered only by skin and small volume of soft tissues, so there are few areas that can withstand the stresses arising from the prosthesis use [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Manufacture Method of Forearm Prosthesis Combined Sockets for Complex Prosthetics

Ivanovich

2018

ETOAJ

View full text Add to dashboard Cite

Background:The shape and dimensions of the stump do not coincide, according to anatomical and physiological considerations, with the shape of the inner surface of the socket. They change during the operation of the prosthesis due to temperature, blood pressure, intensity of the load, concomitant diseases.Objective: Forearm stump of mans with age 40-55years. Reason for amputation is trauma.Method: Develop designs of combined sockets depending on the defect of the stump and its level with embedded covers or elements dampening peak loads and measuring piston movements with different sockets.Results: Stump reciprocating movement in the prosthetic socket was reduced. Conclusion:Embedded covers and elements dampening peak loads allow to realize better feedback. Patients get one more factor to use their prosthetic in daily life.

show abstract

An implementation of movement classification for prosthesis control using custom-made EMG system

Cited by 4 publications

References 4 publications

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Applying EMG technology in medial and lateral elbow enthesopathy treatment using Myo motion controller

Manufacture Method of Forearm Prosthesis Combined Sockets for Complex Prosthetics

Contact Info

Product

Resources

About