The study of principle component of the surface electromyography signal of the Bicep Brachii muscle

Sabri, M. I. A.; Miskon, Muhammad Fahmi; Basri, Abd Samad Hasan; Yaacob, Mohd Rusdy

doi:10.1109/roma.2014.7295853

Cited by 1 publication

(1 citation statement)

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“…Signal noises, data collection methods, and environmental factors pose significant hurdles that must be overcome to ensure the robustness and reliability of EMG pattern recognition [4][5][6]. To address these challenges, researchers have relied on key references such as "Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal" by Vijayvargiya et al, [7], and "The study of principle component of the surface electromyography signal of the Bicep Brachii muscle" by Sabri et al, [8]. In the pursuit of accurate EMG analysis, the development of recommendations for SEMG sensors and sensor placement procedures by Hermens et al, [9] and study of EMG sensor to muscle serves as a valuable resource [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Electromyography Indices of Handgrip Force with Swinging Motion

Wan Hisarudin Wan Abdullah,

Wan Mohd Bukhari Wan Daud,

Mohammad Osman Tokhi

et al. 2023

ARASET

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Electromyography (EMG) is a powerful tool for studying muscle activity, but there is limited research on EMG signals in the muscles of the human forearm, which poses challenges for prosthetic hand development. This study utilized the maximum voluntary contraction (MVC) normalization method to analyze the flexor carpi radialis muscle during handgrip and swinging motions. The MVC indices revealed a significant proportion of high-amplitude MVC results. We conducted three statistical analyses to validate the indices. One-way ANOVA showed significant differences in mean values among the seven subjects during the percent MVC test. RMS study demonstrated a linear correlation between muscle contraction and movement. Boxplot analysis revealed variations within the interquartile range and median values across the entire MVC range. To achieve these results, we employed an eighth-order Gaussian function for curve fitting and exponential weighted moving average. The median interquartile range showed high discrepancies, while the differences between MVC increments were minimal, providing reliable indices for swinging motion. This suggests that the fat layer thickness may influence the muscle signal's frequency characteristics. In summary, our study highlights the untapped potential of EMG signals in the forearm muscles for prosthetic hand development. By employing MVC normalization and conducting rigorous statistical analyses, we uncovered significant findings that contribute to advancements in this field. Our insights provide hope and inspiration for researchers and practitioners seeking to enhance prosthetic technology.

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