THE JOURNAL OF KMUTNB Volume 31, No. 04, Month OCTOBER, Year 2021, Pages 614 - 623
Evaluation of artificial neural networks structures for human activity recognition and development of a computerized prosthetic leg
Uttapon Khawnuan, Nantakrit Yodpijit, Manutchanok Jongprasithporn
Abstract Download PDFHuman activity recognition system for computerized prosthetic leg can help the wearers to have a more comfortable and natural movement. In this study, an artificial neural network (ANN) model was constructed to classify movement activities based on accelerometer and gyroscope sensors. The publicly available walking dataset was collected from 30 subjects aged between 19–48 years. Each subject performed 6 activities (normal walking, upstairs walking, downstairs walking, sitting, standing and laying). This dataset was used for training and testing the ANN model. A number of neurons in the hidden layer were set by changing them from 1 to 200 with an interval of 1 until the network with most accuracy sensitivity and specificity was collected. The feedforward neural network (FFNN) trained using backpropagation (BP) was used to build the ANN model. The results showed that the optimal number of neurons in the hidden layer was 73. This ANN model can be applied in the development of computerized prosthetic leg.
Artificial Neural Network; Human Activity Recognition System; Computerized Prosthetic Leg; Accelerometer and Gyroscope Sensors