Mario Broeckx

Dynamic 3D scanning as a markerless method to calculate multi-segment foot kinematics during stance phase: Methodology and first application

Multi-segmental foot kinematics have been analyzed by means of optical marker-sets or by means of inertial sensors, but never by markerless dynamic 3D scanning (D3DScanning). The use of D3DScans implies a radically different approach for the construction of the multi-segment foot model: the foot anatomy is identified via the surface shape instead of distinct landmark points. We propose a 4-segment foot model consisting of the shank (Sha), calcaneus (Cal), metatarsus (Met) and hallux (Hal). These segments are manually selected on a static scan. To track the segments in the dynamic scan, the segments of the static scan are matched on each frame of the dynamic scan using the iterative closest point (ICP) fitting algorithm. Joint rotations are calculated between Sha-Cal, Cal-Met, and Met-Hal. Due to the lower quality scans at heel strike and toe off, the first and last 10% of the stance phase is excluded. The application of the method to 5 healthy subjects, 6 trials each, shows a good repeatability (intra-subject standard deviations between 1° and 2.5°) for Sha-Cal and Cal-Met joints, and inferior results for the Met-Hal joint (>3°). The repeatability seems to be subject-dependent. For the validation, a qualitative comparison with joint kinematics from a corresponding established marker-based multi-segment foot model is made. This shows very consistent patterns of rotation. The ease of subject preparation and also the effective and easy to interpret visual output, make the present technique very attractive for functional analysis of the foot, enhancing usability in clinical practice.

Towards analysis of foot motion using dynamic 3d surface scanning

Until now foot motion is mostly studied by markerbased 3D analysis. However, a recently developed dynamic 3D scanner is able to scan the entire foot surface at 49 Hz (Vialux). This creates a variety of possibilities for analysis of foot motion during gait. The aim of this study is the extraction of a segmented foot model from a dynamic 3D scan. Such a foot model would enable a motion analysis without the need to predefine segments, with data from quick and non-invasive measurements.

Eigenfoot decomposition of plantar pressure images and case study of feature prediction of two modalities.

The registration of plantar pressure images is a widely used technique to support human gait analysis. In plantar pressure images, most of the time conventionally derived features are used for further processing. Recently, automatic feature extraction based on PCA and kPCA is being used, to increase the information that can be extracted from this data. In this paper, we describe our work flow and a case study on the application of predicting two pressure features and a non-pressure feature out of the automatically derived PCA features. This includes the normalization of the pressure images, the PCA based feature extraction, and building and testing the regression model based on a linear and kernel SVM.

Relation between experts’ foot labels and quantitative foot measurements

momentum of the right hand before delivering the ball on the strike path. The shock pulse on the heel is due to landing on the left leg after hard acceleration and braking that gives the body the momentum to be passed on to the ball during delivery. This left foot placement provides both stability and acts as a shock absorber which ensures optimal function and success of the left leg/right arm delivery action. Higher levels of loading at the medial part of the hindfoot when wearing shoes in both samples indicate specificity of technique for the delivery leg providing the optimal position of the body and release/ delivery of the ball. Disclosure statement

Prediction of clinical foot characteristics using quantitative features from different measurement set-ups

We measured 77 healthy subjects without major foot deformities. They were all clinically assessed by 10 experts (orthopedic technologists, podiatrists, and one orthopedic surgeon), hence a total of 770 assessments. Furthermore, an anamnesis was conducted, and gait of all subjects was quantitatively measured using three-dimensional (3D) motion analysis (Codamotion), dynamic pressure plate (RSScan International), a dynamic 3D scanner (ViALUX), and a force plate (AMTI). To identify those clinical characteristics, which are robust over the different experts, we conducted a 2agreement weighted kappa analysis which is an extension of Cohen’s kappa for multiple raters (Warrens, 2012). Furthermore, we included both the popularity and the discriminative power of a characteristic (i.e. how many experts scored it and how diverse are the scores, respectively.). We included these last two elements because if either popularity or discriminative power are low, we cannot say much about a certain feature, e.g. if it is evaluated by only one or two experts, or if all subjects get the same score. In a second part, we used the quantitatively extracted features (from the pressure plate, 3D motion analysis, dynamic 3D scanner, and force plate) to predict the average expert scores, for each clinical characteristic individually. To determine the best feature subset, we carried out a feature selection using the Lasso technique in a 10-fold cross validation. The feature subset was then fed to a support vector machine (SVM) classifier which trained a prediction model using a leave-one-out crossvalidation. Finally, from these data we can give an indication which hardware is best to predict foot characteristics. To this end, we built the SVM model only including features from one or a limited set of measurement equipment. In this abstract, we highlight three cases: prediction of the resting calcaneal stance position (RCSP), pressure of the midfoot during stance, and the ratio of the forefoot/heel width.

Comparison of biomechanical foot analyses between nine Flemish foot-experts

Treatment or prevention of specific foot problems often requires an analysis of the biomechanics of the foot. These analyses can be performed by different experts. Specifically, in Flanders, they may be performed by medical doctors in orthopaedics and rehabilitation, orthopaedic technologists, or podiatrists. It is well known that there is no standardization yet of clinical methods to analyse foot biomechanics [1,2]. The purpose of this study was to investigate to what extent foot experts differ in biomechanical foot analyses. The presented data is a pilot study on 6 subjects, analysed by 9 experts. The complete study will be performed on 78 subjects by 10 experts. In that larger study, all subjects will also be analysed with advanced gait analyses methods. This to correlate the clinical data to objective, quantitative data, and develop foot typology.

Development of a model to analyse foot biomechanics using dynamic 3D surface scanning.

The foot is a complex structure, bearing the responsibility for the load and propulsion of the entire human body during walking. Important variations in foot form and structure exist individually, and complaints of biomechanically related foot problems are a well-known issue. Appropriate analysis of the foot motion during a core activity like walking is required for the prevention, diagnosis and treatment of these foot problems. The complex kinematics of the foot during rollover are mostly studied by 2D video analysis or marker-based 3D motion analysis (Vertommen 2012). However, these techniques are not able to gather full information about the 3D dynamic foot shape while walking. Detailed 3D data on the foot shape changes during rollover contain essential information on foot function and behaviour which are difficult or not detectable with the current measurement techniques. A recently developed dynamic 3D scanner (Vialux) is now able to scan the entire foot surface at 49Hz. In combination with force assessment, this innovative measurement technique has important potential in the field of biomechanics and motion analysis of the foot. To accomplish the extraction of relevant parameters from the 3D dynamic surface data, an appropriate foot model has to be developed. Such a model has the capacity to result in kinematic variables concerning motion of the bones and joints, as well as superimposed shape variables induced by soft tissue deformation in the foot. The aim of this study is the extraction of such a foot model from a dynamic 3D surface scan.