TY - GEN
T1 - Predicted information gain and convolutional neural network for prediction of gait periods using a wearable sensors network
AU - Martinez Hernandez, Uriel
AU - Rubio-Solis, Adrian
N1 - Funding Information:
*This work was supported by the Royal Society Research Grants for the ‘Touching and feeling the immersive world’ project (RGS/R2/192346). Uriel is with the inte-R-action Lab and the Centre for Autonomous Robotics (CENTAUR), Department of Electronic and Electrical Engineering, University of Bath, UK u.martinez@bath.ac.uk Adrian is with the Faculty of Mathematical and Physical Sciences, University College London, London, UK a.rubio-solis@ucl.ac.uk
Publisher Copyright:
© 2021 IEEE.
PY - 2021/12/31
Y1 - 2021/12/31
N2 - 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
AB - 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
UR - http://www.scopus.com/inward/record.url?scp=85115054723&partnerID=8YFLogxK
U2 - 10.1109/RO-MAN50785.2021.9515395
DO - 10.1109/RO-MAN50785.2021.9515395
M3 - Chapter in a published conference proceeding
SN - 978-1-6654-4637-2
T3 - 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
SP - 1132
EP - 1137
BT - 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
PB - IEEE
ER -