Myoelectric prosthesis requires a sensor that can reliably capture surface electromyography (sEMG) signal from amputees for its controlled operation. The main problems with the presently available EMG devices are their extremely high cost, large response time, noise susceptibility, less amplitude sensitivity, and larger size. This paper proposes a compact and affordable EMG sensor for the prosthetic application. The sensor consists of an electrode interface, signal conditioning unit, and power supply unit all encased in a single package. The performance of dry electrodes employed in the skin interface was compared with the conventional Ag/AgCl electrodes, and the results were found satisfactory. The envelope detection technique in the sensor based on the tuned RC parameters enables the generation of smooth, faster, and repeatable EMG envelope irrespective of signal strength and subject variability. The output performance of the developed sensor was compared with commercial EMG sensor regarding signal-to-noise ratio, sensitivity, and response time. To perform this, EMG data with both devices were recorded for 10 subjects (3 amputees and 7 healthy subjects). The results showed 1.4 times greater SNR values and 45% higher sensitivity of the developed sensor than the commercial EMG sensor. Also, the proposed sensor was 57% faster than the commercial sensor in producing the output response. The sEMG sensor was further tested on amputees to control the operation of a self-designed 3D printed prosthetic hand. With proportional control scheme, the myoelectric hand setup was able to provide quicker and delicate grasping of objects as per the strength of the EMG signal.
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http://dx.doi.org/10.1007/s13534-019-00130-y | DOI Listing |
Front Neurol
December 2024
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Purpose: This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of upper limb injuries post-stroke, using surface electromyographic (sEMG) signals and joint motion data.
Methods: The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data. The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features.
Sci Rep
December 2024
Advanced Manufacturing Institute, King Saud University, Riyadh, 11421, Saudi Arabia.
Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues.
View Article and Find Full Text PDFFront Sports Act Living
December 2024
Faculty of Physical Education-Abo Qir, Alexandria University, Alexandria, Egypt.
[This corrects the article DOI: 10.3389/fspor.2024.
View Article and Find Full Text PDFBiomed Eng Online
December 2024
Department of Clinical Physiology, Motion Analysis Center, University Hospital of Toulouse, Hôpital de Purpan, Toulouse, France.
Background: Stroke is the leading cause of acquired motor deficiencies in adults. Restoring prehension abilities is challenging for individuals who have not recovered active hand opening capacities after their rehabilitation. Self-triggered functional electrical stimulation applied to finger extensor muscles to restore grasping abilities in daily life is called grasp neuroprosthesis (GNP) and remains poorly accessible to the post-stroke population.
View Article and Find Full Text PDFMedicine (Baltimore)
December 2024
Department of Rehabilitation Medicine, School of Medicine, Daegu Catholic University, Daegu, Republic of Korea.
Background: Caregiver burden significantly affects both patients and caregivers but is often overlooked in clinical practice. Physical and emotional strain on caregivers can compromise the quality of care. Care robots are emerging as solutions to alleviate these burdens by assisting with routine tasks, thereby reducing caregivers' physical strain and stress.
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