Research Information System
Thesis Type: Postgraduate
Institution Of The Thesis: Cukurova University, Fen Bilimleri Enstitüsü, Biyomedikal, Turkey
Approval Date: 2020
Thesis Language: Turkish
Student: AYBİKE PİROL
Supervisor: Cabbar Veysel Baysal
Abstract:
Classification of surface electromyography (sEMG) signals, which contain
movement information of muscles, are used to achieve a natural working
order in systems for rehabilitation. In this thesis, sEMG signals of
upper extremity biceps and triceps muscles were classified with Long
Short Term Memory (LSTM) artificial neural network which is a Deep
Learning method, in order to estimate elbow joint angle. LSTM model
successfully and effectively made elbow joint angle estimation by
obtaining high accuracy using varying average value and envelope peak
value of sEMG. The results are compared with the elbow joint angle
estimation of the Multi-Layer Sensor (MLP) which uses the attributes of
the sEMG signals. The designed LSTM model has shown that it can estimate
elbow joint angle more effectively in terms of calculation, by
obtaining accuracy as high as MLP.