Putra, Darma Setiawan (2016) Klasifikasi Sinyal EMG Pada Otot Tungkai Selama Berjalan Menggunakan Learning Vector Quantization - Classification Of EMG In Lower Limb Muscle During Walking Using Learning Vector Quantization. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sinyal electromyography (EMG) adalah aktifitas listrik yang terjadi pada
lapisan otot selama adanya gerakan aktif. Gaya berjalan seseorang akan dipengaruhi
oleh struktur tulang dan otot sehingga gaya berjalan tersebut adalah unik. Keunikan
ini dapat digunakan untuk data biometrik. Dalam penelitian ini, kami akan
melakukan klasifikasi data EMG untuk 8 otot tungkai selama berjalan yaitu Rectus
Femoris, Vastus Medialis, Vastus Lateralis, Bicep Femoris, Semitendinosus,
Gastrocnemius Medialis, Gastrocnemius Lateralis, dan Tibialis Anterior. 6 orang
subyek sehat ditempelkan 8 elektroda EMG pada otot tungkai dan diminta untuk
berjalan secara normal sesuai dengan kecepatan masing-masing di laboratorium gait.
Masing-masing subyek berjalan sebanyak 1 siklus berjalan (gait cycle) dan 3 kali
pengambilan data (walking trial). Total data pengambilan adalah sebanyak 18 buah
dataset untuk analisis dan klasifikasi. Metode graph feature extraction dan principal
component analysis digunakan untuk mengekstraksi fitur data EMG dari 8 otot
tungkai selama berjalan. Metode learning vector quantization (LVQ) digunakan
untuk mengklasifikasi data EMG berdasarkan subyek. Metode pembelajaran dan
pengujian data pada jaringan LVQ menggunakan metode validasi silang (cross
validation). Akurasi klasifikasi rata-rata menggunakan metode graph feature
extraction diperoleh sebesar 88.89% dan metode PCA diperoleh sebesar 66.67%.
Dari hasil ini menunjukkan bahwa sinyal EMG selama berjalan dari 8 otot tungkai
dapat digunakan sebagai identitas biometrik gait.
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Electromyography (EMG) signal is an electrical activity that occurs in the
muscle layer during active motion. The way people walking is defined by the
structure of individual muscle and bones so that the way of walking is unique and
must be able to used in biometric data. In this study, we classified the EMG data dari
8 lower limb muscle during normal walking test (Rectus Femoris, Vastus Medialis,
Vastus Lateralis, Bicep Femoris, Semitendinosus, Gastrocnemius Medialis,
Gastrocnemius Lateralis, and Tibialis Anterior). Six healthy volunteer were
involving in this study by walking in GaitLab with 8 EMG electrodes attached on
their muscle. Each volunteer performed one gait cycle and 3 walking trial. So in total
18 EMG dataset were analized for classification. Graph feature extraction and
principal component analysis method was used to extract the feature of EMG data of
all 8 muscle during walking. Learning Vector Quantization (LVQ) was used to
classify the EMG data based on subject. Training and testing method in LVQ network
used cross validation (CV). The average accuracy of classification using graph
feature extraction method is 88.89% and using PCA method is 66.67%. In the result
show that EMG data during walking of 8 lower limb muscles can be used to identity
of gait biometric.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 616.740 754 7 Put k |
| Uncontrolled Keywords: | EMG, Otot Tungkai, Biometrik Gait, Principal Component Analysis, Analisis Gait. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3226 Transients (Electricity). Electric power systems. Harmonics (Electric waves). |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering |
| Depositing User: | ansi aflacha |
| Date Deposited: | 17 Dec 2019 06:42 |
| Last Modified: | 17 Dec 2019 06:42 |
| URI: | http://repository.its.ac.id/id/eprint/72398 |
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