Publication: A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis using Inertial Sensors
Paper Submission by our Partner Fraunhofer AICOS
Vânia Guimarães and her team at Fraunhofer AICOS succefully published a paper in Sensors: Sensors is the leading international, peer-reviewed, open access journal on the science and technology of sensors.
A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis using Inertial Sensors
Gait performance is an important marker of motor and cognitive decline in older adults. Instrumented gait analysis resorting to inertial sensors allow 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 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 reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. Good agreement is also achieved between 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.
Link to the paper: https://doi.org/10.3390/s21227517.