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The real-world measurement of minimum foot clearance (mFC) during the swing phase of gait is critical in efforts to understand and reduce the risk of trip-and-fall incidents in populations with gait impairments. Past research has focused on measuring clearance of a single point on a person’s foot, typically the toe—however, this may overestimate mFC and may even be the wrong region of the foot in cases of gait impairments or interventions. In this work, we present a novel method to reconstruct the swing-phase trajectory of an arbitrary number of points on a person’s shoe and estimate the instantaneous height and location of whole-foot mFC. This is achieved using a single foot-mounted inertial sensor and personalized shoe geometry scan, assuming a rigid-body IMU-shoe system. This combination allows collection and analysis using out-of-lab tests, potentially including clinical environments. Validation of single marker location using the proposed method vs. motion capture showed height errors with bias less than 0.05 mm, and 95% confidence interval of − 8.18 to + 8.09 mm. The method is demonstrated in an example data set comparing different interventions for foot drop, and it shows clear differences among no intervention, functional electrical stimulation, and ankle–foot orthosis conditions. This method offers researchers and clinicians a rich understanding of a person’s gait by providing objective 3D foot kinematics and allowing a unique opportunity to view the regions of the foot where minimum clearance occurs. This information can contribute to a more informed recommendation of specific interventions or assistive technology than is currently possible in standard clinical practice. [ABSTRACT FROM AUTHOR]

Frontal plane ankle motion is important for balance in walking but is seldom controlled in robotic prostheses. This article describes the design, control and performance of a semi-active two-degree-of-freedom robotic prosthetic ankle. The mechanism uses a non-backdrivable wedge cam system based on rotating inclined planes, allowing actuation only during swing phases for low power, light weight and compactness. We present details of the mechanism and its kinematic and mechatronic control, and a benchtop investigation of the system's speed and accuracy in ankle angle control. The two-axis ankle achieves angular reorientation movements spanning ±10 deg in any direction in less than 0.9 s. It achieves a plantarflexion/dorsiflexion error of 0.35 ± 0.27 deg and an inversion/eversion error of 0.29 ± 0.25 deg. Backdriven motion during walking tests is negligible. Strengths of the design include self-locking behavior for low power and simple kinematic control. Two-axis ankle angle control could enable applications such as balance augmentation, turning assistance, and wearable perturbation training. [ABSTRACT FROM AUTHOR]

Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are 0.03 ± 0.08   c m on level ground, 0.95 ± 0.37   c m on ramps, and 1.27 ± 1.22   c m on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs. [ABSTRACT FROM AUTHOR]