LAUSR

Estimation of center of mass (CoM) and center of pressure (CoP) is critical for lower limb exoskeletons, prostheses, and legged robots. To meet the demand in these fields, this study presents a novel CoM and CoP estimation method for human walking through a wearable visual odometry (VO) device. This method is named VO-based estimation of CoM and CoP (VOECC). The methodology of VOECC is that the VO provides CoM trajectory estimation and the inherent walking dynamics model is exploited as prior knowledge for CoP trajectory estimation during human walking. Gait cycle is estimated based on the frequency analysis of the CoM trajectory, which is cropped into segments. Each segment mainly includes a half gait cycle. The segments are designed to be sliding to mitigate the disturbance of double-stance phase. For each segment, a quadratic programming (QP) problem is formulated to fit the CoM measurement with the theoretical walking dynamics model. The solution to this QP problem is an optimal gait parameters estimation, including CoP. Based on this solution, the human walking model with the CoM trajectory and CoP excursion is reconstructed. VOECC is evaluated experimentally where human walks on level ground and upstairs with VO device attached in front of the chest. The ground truth of CoM and CoP position is directly measured by the motion capture system and fully instrumented treadmill, respectively, and compared with the VOECC results. The proposed method is demonstrated to be effective in terms of wearable and extensible functionalities compared with the existing methods. Root-mean-squared errors between the CoP measured by fully instrumented treadmill and the CoP estimated by VOECC are evaluated and compared. This method has the potential to be extensible in lower limb rehabilitation, prosthetic, and legged locomotion fields. Note to Practitioners—This article addresses the problem of estimating center of mass (CoM) and center of pressure (CoP) trajectories using a minimum number of wearable sensors and reliable algorithms during human daily walking. Estimation of CoM and CoP trajectories is critical for lower limb exoskeletons, prostheses, and legged robots. In this study, a novel method named VO-based estimation of CoM and CoP (VOECC) is presented that utilizes a walking model as prior knowledge and integrates it with visual odometry data, which estimates the trajectory of the wearable visual device. Compared with the inertia measurement unit (IMU)-based method, VOECC only uses one wearable visual device and thus significantly reduces the cost and system complexity. In addition, the VOECC outperforms the motion capture system and force plate since it is not limited to space constraints and has the potential to be applicable for daily life locomotion tasks. VOECC is wearable and untethered and therefore can be directly amounted on lower limb exoskeletons, prostheses, and legged robots. In the experiments, motion capture system and force plates are used to measure CoM and CoP as ground truth to demonstrate the effectiveness of the proposed VOECC. Practical limitations include failure from fast turning and synchronization of multichannel sensors. These limitations will be addressed as our future research directions.