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Article

A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

1
Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
2
Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
3
INESC TEC (Institute for Systems and Computer Engineering, Technology and Science), 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Felix Kluge
Sensors 2021, 21(22), 7517; https://doi.org/10.3390/s21227517
Received: 11 October 2021 / Revised: 5 November 2021 / Accepted: 10 November 2021 / Published: 12 November 2021
Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions. View Full-Text
Keywords: inertial sensors; gait analysis; foot trajectory; deep learning; long short-term memory (LSTM) networks inertial sensors; gait analysis; foot trajectory; deep learning; long short-term memory (LSTM) networks
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MDPI and ACS Style

Guimarães, V.; Sousa, I.; Correia, M.V. A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors. Sensors 2021, 21, 7517. https://doi.org/10.3390/s21227517

AMA Style

Guimarães V, Sousa I, Correia MV. A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors. Sensors. 2021; 21(22):7517. https://doi.org/10.3390/s21227517

Chicago/Turabian Style

Guimarães, Vânia, Inês Sousa, and Miguel V. Correia. 2021. "A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors" Sensors 21, no. 22: 7517. https://doi.org/10.3390/s21227517

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