Abstract
An accurate detection of the gait sub-phases is fundamental in clinical gait analysis to interpret kinetic and kinematic data. In general, detecting the gait events that mark the transition from one gait sub-phase to another as well as the sequence of sub-phases is essential to evaluate gait abnormalities. However, finding a reliable segmentation for pathological gait has been a challenging task. This manuscript entails a generic approach for the gait segmentation into sub-phases in the CORBYS1 system. A number of distinctive features are extracted from the Hip joints motion data which are able to partition and segment the gait cycles in an efficient way. The degree of deviation (i.e. anomaly) in each sub-phase is then calculated with respect to an optimal gait reference which is used for robot-assisted gait rehabilitation. The proposed gait segmentation method is applicable to gait with many types of pathology since training on the pathology specific templates is not required. Performance of the proposed algorithm is evaluated by statistical analysis of results which produced 100% gait segmentation accuracy for healthy subjects and over 99% for pathological subjects.
Highlights
Gait analysis has by far received the most study as compared to other human body movement [1] because of its readily observable features
Swing and stancephases are further divided into seven sub-phases that include, Initial Contact (IC), Load Response (LR), Mid Stance (MSt), Terminal Stance (TSt), Pre-Swing (PSw), Initial Swing (ISw), Mid- Swing (MSw), and Terminal Swing (TSw) [2,3]
We indicated the subject’s health condition, accuracy for complete cycle partition in the gait data through manual and automatic procedures, the Abnormality Detection accuracy for a gait to be recognised as normal or pathological, and accuracy of sub-phase level segmentation for healthy and patient gait data corresponding to the varying temporal tolerance window
Summary
Gait analysis has by far received the most study as compared to other human body movement [1] because of its readily observable features. Gait comprises a periodic phenomenon in both legs in such a way that the entire body moves from one place to another [2]. A single gait cycle consists of one stride length which is further divided into stance and swing phases. One leg bears the weight of the body whereas the swing phase causes the leg’s forward motion to propel the body. Since a measurement of the gait with foot switches, gyroscopes, and accelerometers can only detect events that are caused by repetitive patterns in the movement and in measured signals, the phases detected from sensors differ from those based on functional tasks. Literature contains a variety of gait segmentation models consisting of a different number of sub-phases varying from two to seven depending upon different clinical aims [4]
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