Using a texture analyser to objectively quantify foot orthoses.

BACKGROUND AND AIM: Foot orthoses alter the kinematics and kinetics in gait. With the increasing importance of evidence based practice and with the permanent development of subtractive manufacturing and introduction of additive manufacturing, there is a growing need for quantification of orthoses parameters. We describe a measurement method and protocol to quantify different parameters of a foot orthosis. TECHNIQUE: A texture analyser is used to impose a displacement of the surface of the orthosis, while the applied force is measured. The measured points are determined based on the location of anatomical landmarks on the foot. Out of the measured data, parameters are calculated representing the stiffness, compression set and shape. DISCUSSION: To illustrate the proposed technique, five different parameters are extracted from three example orthoses. Results show the added value of the proposed technique as the parameters are not only defined by the material but also by the shape.

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.

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.