A perceptual map for gait symmetry quantification and pathology detection.

BACKGROUND: The gait movement is an essential process of the human activity and the result of collaborative interactions between the neurological, articular and musculoskeletal systems, working efficiently together. This explains why gait analysis is important and increasingly used nowadays for the diagnosis of many different types (neurological, muscular, orthopedic, etc.) of diseases. This paper introduces a novel method to quickly visualize the different parts of the body related to an asymmetric movement in the human gait of a patient for daily clinical usage. The proposed gait analysis algorithm relies on the fact that the healthy walk has (temporally shift-invariant) symmetry properties in the coronal plane. The goal is to provide an inexpensive and easy-to-use method, exploiting an affordable consumer depth sensor, the Kinect, to measure the gait asymmetry and display results in a perceptual way. METHOD: We propose a multi-dimensional scaling mapping using a temporally shift invariant distance, allowing us to efficiently visualize (in terms of perceptual color difference) the asymmetric body parts of the gait cycle of a subject. We also propose an index computed from this map and which quantifies locally and globally the degree of asymmetry. RESULTS: The proposed index is proved to be statistically significant and this new, inexpensive, marker-less, non-invasive, easy to set up, gait analysis system offers a readable and flexible tool for clinicians to analyze gait characteristics and to provide a fast diagnostic. CONCLUSION: This system, which estimates a perceptual color map providing a quick overview of asymmetry existing in the gait cycle of a subject, can be easily exploited for disease progression, recovery cues from post-operative surgery (e.g., to check the healing process or the effect of a treatment or a prosthesis) or might be used for other pathologies where gait asymmetry might be a symptom.

Local symmetry detection in natural images using a particle filtering approach.

In this paper, we propose an algorithm to detect smooth local symmetries and contours of ribbon-like objects in natural images. The detection is formulated as a spatial tracking task using a particle filtering approach, extracting one part of a structure at a time. Using an adaptive local geometric model, the method can detect straight reflection symmetries in perfectly symmetrical objects as well as smooth local symmetries in curved elongated objects. In addition, the proposed approach jointly estimates spine and contours, making it possible to generate back ribbon objects. Experiments for local symmetry detection have been conducted on a recent extension of the Berkeley segmentation data sets. We also show that it is possible to retrieve specific geometrical objects using intuitive prior structural information.

Evaluating perceptual maps of asymmetries for gait symmetry quantification and pathology detection.

The gait movement is a complex and essential process of the human activity. Yet, many types of diseases (neurological, muscular, orthopedic, etc.) can be diagnosed from the gait analysis. This paper introduces a novel method to quickly visualize the different body parts related to an (temporally shift-invariant) asymmetric movement in the human gait of a patient for daily clinical usage. The goal is to provide a cheap and easy-to-use method that measures the gait asymmetry and display results in a perceptual and intuitive way. This method relies on an affordable consumer depth sensor, the Kinect, which is very suitable for small room and fast diagnostic, since it is easy to setup and marker-less.