Hindfoot motion analysis by subtalar compensation and ankle osteoarthritis stage using a multi-segment foot model

Background The biomechanics of the hindfoot in ankle osteoarthritis (OA) are not yet fully understood. Here we aimed to identify hindfoot motion in a gait analysis using a multi-segment foot model (MFM) according to ankle OA stage or hindfoot alignment by the presence of subtalar compensation. Methods We retrospectively reviewed the medical records, simple radiographs, and gait MFM data of 54 ankles admitted to our hospital for the treatment of advanced ankle OA. Spatiotemporal gait parameters and three-dimensional motions of the hindfoot segment were analyzed according to sex, age, body mass index, Takakura classification, and presence of subtalar compensation. Results No spatiotemporal gait parameters differed significantly according to the presence of subtalar compensation or ankle OA stage. Only normalized step width differed significantly (P = .028). Average hindfoot motion (decompensation versus compensation) did not differ significantly between the sagittal and transverse planes. Graphing of the coronal movement of the hindfoot revealed collapsed curves in both groups that differed significantly. Compared with Takakura stages 3a, 3b, and 4, cases of more advanced stage 3b had a smaller sagittal range of motion than those of stage 3a (P = .028). Coronal movement of the hindfoot in cases of Takakura stage 3a/3b/4 showed a relatively flat pattern. Conclusions The spatiotemporal parameters were not affected by the alignment state of the heel resulting from subtalar compensation. The sagittal range of hindfoot motion decreased in patients with advanced ankle OA. Once disrupted, the coronal movement of the subtalar joint in ankle OA did not change regardless of ankle OA stage or hindfoot compensation state.

Vision-Based Approach in Contact Modelling between the Footpad of the Lander and the Analogue Representing Surface of Phobos

Identifying solar system surface properties of celestial bodies requires the conducting of many tests and experiments in conditions similar to those found on various objects. One of the first tasks to be solved by engineers is determining the contact condition between the lander and the surface of a given celestial body during landing in a microgravity environment. This paper presents the results of experimental studies and numerical simulations of the contact phenomenon between the lander foot model and the Phobos analogue. The main goal of the experimental tests was to obtain measured deformation data of the studied analogues using 2D and 3D vision systems, which were employed to analyze the behavior of the lander foot and the surface of the studied analogue itself and to calibrate the numerical models. The analogue representing the Phobos surface was foam concrete. The variable parameters in the study were the analogue thickness and the lander foot velocity at the time of contact. Tests were conducted for three different contact velocities of 1.2 m/s, 3.0 m/s, and 3.5 m/s. Taking into account the mass of the lander foot model, kinetic energies of 30.28 J, 189.22 J, and 257.56 J were obtained. The results showed that at low contact velocities, and thus low kinetic energies, no significant differences in behavior of the material directly under the lander foot were observed, and similar values of forces in the lander foot were obtained. For higher contact velocities, the behavior of analogues with varying thicknesses was different, resulting in different values of analogue deformation and dynamics of increments and decrements of force in the lander foot itself. Although performed on a single material, the experiments revealed different behaviors depending on its thickness at the same impact energy. This is an essential guideline for engineers who need to take this fact into account when designing the lander itself.

A Position Sensing Glove to Aid Ankle-Foot Orthosis Diagnosis and Treatment

A position sensing glove called SmartScan, which creates a 3D virtual model of a real object, is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, having a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.