Predicted information gain and convolutional neural network for prediction of gait periods using a wearable sensors network

Uriel Martinez Hernandez, Adrian Rubio-Solis

Research output: Chapter in Book/Report/Conference proceedingChapter in a published conference proceeding

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Abstract

This work presents a method for recognition of walking activities and prediction of gait periods using wearable sensors. First, a Convolutional Neural Network (CNN) is used to recognise the walking activity and gait period. Second, the output of the CNN is used by a Predicted Information Gain (PIG) method to predict the next most probable gait period while walking. The output of these two processes are combined to adapt the recognition accuracy of the system. This adaptive combination allows us to achieve an optimal recognition accuracy over time. The validation of this work is performed with an array of wearable sensors for the recognition of level-ground walking, ramp ascent and ramp descent, and prediction of gait periods. The results show that the proposed system can achieve accuracies of 100% and 99.9% for recognition of walking activity and gait period, respectively. These results show the benefit of having a system capable of predicting or anticipating the next information or event over time. Overall, this approach offers a method for accurate activity recognition, which is a key process for the development of wearable robots capable of safely assist humans in activities of daily living
Original languageEnglish
Title of host publication2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
PublisherIEEE
Pages1132-1137
Number of pages6
ISBN (Electronic)978-1-6654-0492-1
ISBN (Print)978-1-6654-4637-2
DOIs
Publication statusPublished - 31 Dec 2021

Publication series

Name2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Communication
  • Artificial Intelligence

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