From the Classification of EMG Signals to the Development of a New Lower Arm Prosthesis

Lisi, Giuseppe; Belluco, Paolo; Cattaneo, Dario; Gini, Giuseppina

doi:10.3182/20110828-6-it-1002.02097

Cited by 8 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The amplitude of these signals is very small and can be ranged from 0 to 10 millivolt (peak-to-peak) or 0 to 5 millivolt (rms). The usable energy of the signal is limited to a specific frequency range of (0 to 500 Hz) [18].…”

Section: Methodsmentioning

confidence: 99%

“…The result was positive but the system was costly and it restricted the movement of the amputees. In [7], the authors developed a lower arm prosthesis based on EMG signals for classifying seven different movements by using three channels with a bipolar configuration, six surface electrodes plus only one another electrode as a reference that were located beside the forearm muscles. Morever, the proposed system offers a high classification accuracy by employing the motions of hand fingers instead of the wrist in order to enhance the performance of prosthesis by doing complex tasks, but the real time response was slower according to the ANN classifier used.…”

Section: Related Workmentioning

confidence: 99%

“…Usually, most frequent causes of upper limb amputation are trauma and cancer, followed by vascular complications of disease of right arm that is more frequently involved in work [2]. The EMG based control system is considered the best utilized system recently when compared with other systems due to its non-invasive behavior [3]. EMG-based control system can be separated into two main groups: Pattern recognition and non-pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Control System Based on Hand Nerve Signals

Abbas¹,

Croock²

2019

IJCCCE

View full text Add to dashboard Cite

Recent advances in the control applications based on hand nerve signals are able to meet the needs of users who suffer from restrictions in limb movement and also provide high performance control for those paralyzed people. These signals are represented as Electromyography (EMG) signals, which are biomedical ones, used for clinical/biomedical applications. In this work, a control signals generation system is proposed based on hand EMG measurements. The process of acquisition and processing of EMG signals is performed by only one channel surface EMG electrodes with one EMG processing unit as a muscle sensor. In this work, Arduino UNO is adopted as an analog to digital converter for these hand nerve signals to be easily analyzed in the classification process. These signals are measured from the skin surface of forearm and biceps muscles in two suggested case studies to be used in generating signals based on ten muscles movements. The main features that crystallized this research is building a smart control algorithm which increases the flexibility of generating precise control signals based on contracted hand movements with high simplicity of use and the low cost. The obtained results are compared to other systems results to show the ability of achieving 93.81% classification rate or accuracy among other systems.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation