Abstract
The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination () is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and among the hip, knee, and ankle joints.
Highlights
Publisher’s Note: MDPI stays neutralGait is the basic method of movement, and gait parameters contain much biomechanical information
Motion capture (MoCap) systems are widely used for accurate gait analysis in the laboratory [8,9]; markers are attached to each subject according to the marker set, and the gait is analyzed by creating a model based on the marker positions
We evaluated the performance of the proposed model by calculating the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R2 ), which are widely used to assess estimation results
Summary
Publisher’s Note: MDPI stays neutralGait is the basic method of movement, and gait parameters contain much biomechanical information. Motion capture (MoCap) systems are widely used for accurate gait analysis in the laboratory [8,9]; markers are attached to each subject according to the marker set, and the gait is analyzed by creating a model based on the marker positions This instrument is difficult to use in a daily life environment because of its high cost, long preparation and setup time, and requirement for a large space [10,11]. The hidden state that is updated based on previous and current information from sequential data allows RNNs to perform better than other general multilayer perceptron neural network models. Previous information in time series data influences the current neural network calculation
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
