TY  - BOOK
AU  - Ikram, Zainab 
AU  - Supervisor : Dr. Muhammad Asim Waris
TI  - A Comparative Analysis of Different Features for EMG Signal Classification
U1  - 610 
PY  - 2024///
CY  - Islamabad : 
PB  - SMME- NUST
KW  - MS Biomedical Sciences (BMS)
N1  - Electromyography (EMG) signals serve as vital tools in neurological and
neuromuscular conditions diagnosis. Various features are used as inputs for pattern
recognition algorithms. This project intends to increase the precision and efficacy of
prosthetic limb control, with the goal of boosting the quality of life for individuals with
limb amputations, using a Linear Support Vector Machine technique. Specifically, we
intend to analyze the usefulness of the distinctive feature known as Cardinality within
diverse combinations of time-domain and frequency-domain features. In order to improve
signal quality, the raw EMG signal is filtered and segmented. The time-domain and
frequency-domain features are then retrieved from overlapping segments, and the most
relevant ones are retained using exhaustive feature selection. An SVM classifier is then
used to examine the possible impact of Cardinality on prosthetic control and
rehabilitation outcomes. The research findings show that the efficiency of Cardinality is
dependent on the precision of the units used. Cardinality performed best when seven
decimal points are used. MAV stands out among time-domain features, as it generated a
high number of combinations with Cardinality, enhancing its performance in myoelectric
pattern recognition and BP emerges as the top-performing frequency-domain feature
when integrated with Cardinality, surpassing other frequency-domain features and
forming the most numerous combinations. The SVM classifier achieved classification
accuracy of 85.58% of M1, 70.49% of M2, 77.32% of M3, 77.24% of M4, 80.82% of
M5, 77.52% of M6, 82.94% of M7, 84.34% of M8, 84.75% of M9, 86.92% of M10 for
the combination of Cardinality with MAV and BP. As advancements in prosthetics and
rehabilitation technologies continue, the insights gained from this study can play a pivotal
role in refining the precision and efficiency of Myoelectric Control systems, ultimately
benefiting individuals with limb loss or motor impairments
UR  - http://10.250.8.41:8080/xmlui/handle/123456789/45023
ER  -