Open AccessHand motion strength forecasting using Extreme Learning Machine for post-stroke rehabilitation
Author(s) -
Khairul Anam,
Ali Rizal Chaidir,
Fahrul Isman
Publication year - 2021
Publication title -
jurnal teknologi dan sistem komputer
Language(s) - English
Resource type - Journals
eISSN - 2620-4002
pISSN - 2338-0403
DOI - 10.14710/jtsiskom.2021.13844
Subject(s) - extreme learning machine , stroke (engine) , rehabilitation , mean squared error , artificial intelligence , computer science , electromyography , strength training , robot , machine learning , physical medicine and rehabilitation , artificial neural network , pattern recognition (psychology) , engineering , mathematics , statistics , physical therapy , medicine , mechanical engineering
Stroke or Cerebrovascular accident (CVA) can cause weakness in one side of the body, including the upper limbs such as the hand. Rehabilitation is needed to restore the function of the hand. Rehabilitation should also measure the strength of the movements carried out. This article aims to forecast the strength of movement based on Electromyography (EMG) signals using the Extreme Learning Machine (ELM). This study collected EMG signal data and movement strength, carried out data pre-processing and data extraction using various extraction features, applied ELM for forecasting strength based on EMG signals, and applied created models in stroke therapy robots. The forecasting model is evaluated by measuring the Mean Squared Error (MSE). The average value of the best MSE in offline testing is 1.77, while the real-time testing is 0.79. A small MSE value indicates that the model is good enough. The resulted value of strength can be applied to make the stroke therapy robots actuating properly.