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.


METHODS
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.


CONCLUSIONS
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.
Département d'Informatique & Recherche Opérationnelle (DIRO), Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, H3C 3J7, Canada. a.moevus@gmail.com.
Antoine
A
Moevus
Département d'Informatique & Recherche Opérationnelle (DIRO), Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, H3C 3J7, Canada. mignotte@iro.umontreal.ca.
Max
M
Mignotte
Laboratoire de Recherche en Imagerie et Orthopédie, Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada. jacques.deguise@etsmtl.ca.
Jacques A
JA
de Guise
Département d'Informatique & Recherche Opérationnelle (DIRO), Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, H3C 3J7, Canada. meunier@iro.umontreal.ca.
Jean
J
Meunier
D000328:Adult; D000465:Algorithms; D004194:Disease; D005260:Female; D005684:Gait; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D010808:Physical Examination / Q000191:economics / Q000379:methods*