Inertial Measurement Unit-Based Estimation of Foot Trajectory for Clinical Gait Analysis

• Published in: Frontiers in physiology 2020-01, Vol.10, p.1530-1530
• Main Authors: Hori, Koyu, Mao, Yufeng, Ono, Yumi, Ora, Hiroki, Hirobe, Yuki, Sawada, Hiroyuki, Inaba, Akira, Orimo, Satoshi, Miyake, Yoshihiro
• Format: Article
• Language: English
• Subjects: abnormal gait; gait analysis; IMU; inertial-measurement unit; Physiology; wearable sensors
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2019.01530

Gait analysis is used widely in clinical practice to evaluate abnormal gait caused by disease. Conventionally, medical professionals use motion capture systems or make visual observations to evaluate a patient's gait. Recent biomedical engineering studies have proposed easy-to-use gait analysis methods employing wearable sensors with inertial measurement units (IMUs). IMUs placed on the shanks just above the ankles allow for long-term gait monitoring because the participant can walk with or without shoes during the analysis. To the knowledge of the authors, no IMU-based gait analysis method has been reported that estimates stride length, gait speed, stride duration, stance duration, and swing duration simultaneously. In the present study, we tested a proposed gait analysis method that uses IMUs attached on the shanks to estimate foot trajectory and temporal gait parameters. Our proposed method comprises two steps: stepwise dissociation of continuous gait data into multiple steps and three-dimensional trajectory estimation from data obtained from accelerometers and gyroscopes. We evaluated this proposed method by analyzing the gait of 19 able-bodied participants (mean age 23.9 years, 9 men and 10 women). Wearable sensors were attached on the participants' shanks, and we measured three-axis acceleration and three-axis angular velocity with the sensors to estimate foot trajectory during walking. We compared gait parameters estimated from the foot trajectory obtained with the proposed method and those measured with a motion capture system. Mean accuracy (± standard deviation) was 0.054 ± 0.031 m for stride length, 0.034 ± 0.039 m/s for gait speed, 0.002 ± 0.020 s for stride duration, 0.000 ± 0.017 s for stance duration, and 0.002 ± 0.024 s for swing duration. These results suggest that the proposed method is suitable for gait analysis, whereas there is a room for improvement of its accuracy and further development of this IMU-based gait analysis method will enable us to use such systems for clinical gait analysis.