Open AccessAccelerometer-based human activity recognition using 1D convolutional neural network
Author(s) -
Stefan Tsokov,
Milena Lazarova,
Adelina Aleksieva-Petrova
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1031/1/012062
Subject(s) - activity recognition , convolutional neural network , computer science , accelerometer , feature selection , artificial intelligence , feature extraction , pattern recognition (psychology) , field (mathematics) , set (abstract data type) , feature (linguistics) , data set , artificial neural network , machine learning , linguistics , philosophy , mathematics , pure mathematics , programming language , operating system
Human activity recognition (HAR) is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The performance of the activity recognition system is highly dependent on the features extracted from the sensor data which makes the selection of appropriate features a very important part of HAR. A 1D CNN model trained on accelerometer data is suggested in the paper for automatic feature extraction in a HAR system. A semi-automatic approach is used that effectively and efficiently determines the number of convolutional layers in the network, the number of kernels and the size of the kernels. The experimental results show that the suggested model outperforms several existing recognition approaches that use the same data set.