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 is working on head kinematics based gait analysis. 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 nodding and horizontal rotation that occurs during walking and talking. Additionally, we have also examined the dynamics of head movement on interpersonal coordination (Hwang et al., 2019). These studies were conducted using a single head-worn inertial measurement unit (IMU), which is minimizing sensor effort.
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 simple gait analysis methods for collecting large sizes of empirical data, which has motivated us to develop a standardized database compatible with cameras, IMUs or heterogenuous sensors systems. This database will facilitate numerous future applications to health caring, rehabilitation and security systems using head-worn devices, such as earbuds and extended reality (XR) headsets.
Scientific publication of our research group on head kinematics based gait analysis:
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