Gait analysis plays a crucial role in public health by measuring and describing various aspects of human walking. These include step length, stride length, step width, cadence, velocity, and more. Gait analysis also involves the measurement of kinetics/kinematics of joint movement, muscular activity, stride characteristics, and energy consumption. Gait analysis contributes to improvement in sports performance, physical education of children, injury protection, and rehabilitation of patients.
Gait analysis involves the examination of the repeated walking motion known as the gait cycle, which consists of two main phases: the stance phase and the swing phase. The stance phase refers to the period when the foot is in contact with the ground, extending from heel strike (HS) to toe-off (TO). Conversely, the swing phase occurs when the foot is swinging in the air, spanning from toe-off to heel strike.
Our research group has been focused on gait analysis incorporating head movement as a reference. We have published articles on various topics including real-time gait event detection (Hwang et al., 2018; Hwang et al., 2021), the correlation between head and lower body joint angles (Hwang & Effenberg, 2022), and the utilization of head trajectory diagrams for gait symmetry analysis (Hwang & Effenberg, 2021). It has been demonstrated that the head oscillation for gait analysis is not affected by head nodding and rotating that occurs during walking and talking. Additionally, our research has also examined the dynamics of interpersonal head movement during rapport analysis (Hwang et al., 2019). These studies were conducted using data obtained from a single head-worn inertial measurement unit (IMU), which requires minimized sensor setup.
Currently, our research incorporates deep learning technology, specifically in the field of gait recognition. Gait recognition is utilized for person identification as well as identifying abnormal or unhealthy gait patterns. The applications of gait recognition span across diverse fields including security, forensic gait analysis, and rehabilitation. We recognize the advantage of utilizing simple gait measurement devices and straightforward gait analysis methods for collecting extensive empirical data, which has motivated us to develop a comprehensive database using this data. This database will facilitate numerous future applications involving head-worn devices, such as earbuds and extended reality (XR) headsets.
Xsens MVN Awinda Station (Paulich et al., 2018)
Xsens Dot (Ribera D’alcala’ et al., 2021)
Hwang, T., & Effenberg, A. (2022). Gait Analysis: Head Vertical Movement Leads to Lower Limb Joint Angle Movements. Digest of Technical Papers – IEEE International Conference on Consumer Electronics, 2022-January. https://doi.org/10.1109/ICCE53296.2022.9730350
Hwang, T. H., & Effenberg, A. O. (2021). Head Trajectory Diagrams for Gait Symmetry Analysis Using a Single Head-Worn IMU. Sensors 2021, Vol. 21, Page 6621, 21(19), 6621. https://doi.org/10.3390/S21196621
Hwang, T. H., Reh, J., Effenberg, A. O., & Blume, H. (2018). Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit. IEEE Transactions on Consumer Electronics, 64(2), 240–248. https://doi.org/10.1109/TCE.2018.2843289
Hwang, T.-H., Reh, J., Effenberg, A. O., Blume, H., Hwang, T.-H., Reh, J., Effenberg, · A O, & Blume, · H. (2021). Validation of Real Time Gait Analysis Using a Single Head-Worn IMU. EKC 2019 Conference Proceedings, 87–97. https://doi.org/10.1007/978-981-15-8350-6_8
Paulich, M., Schepers, M., Rudigkeit, N., & Bellusci, G. (2018). Xsens MTw Awinda: Miniature Wireless Inertial-Magnetic Motion Tracker for Highly Accurate 3D Kinematic Applications. Retrieved April 18, 2023, from www.xsens.com,
Ribera D’alcala’, E., Voerman, J. A., Konrath, J. M., & Vydhyanathan, A. (2021). Xsens DOT Wearable Sensor Platform White Paper.