Segmentation and Classification of Gait Cycles

Abstract

Gait abnormalities can be studied by means of instrumented gait analysis. Foot-switches are useful to study the foot-floor contact and for timing the gait phases in many gait disorders, provided that a reliable foot-switch signal may be collected. Considering long walks allows reducing the intra-subject variability, but requires automatic and user-independent methods to analyze a large number of gait cycles. The aim of this work is to describe and validate an algorithm for the segmentation of the foot-switch signal and the classification of the gait cycles. The performance of the algorithm was assessed comparing its results against the manual segmentation and classification performed by a gait analysis expert on the same signal. The performance was found to be equal to 100% for healthy subjects and over 98% for pathological subjects. The algorithm allows determining the atypical cycles (cycles that do not match the standard sequence of gait phases) for many different kinds of pathological gait, since it is not based on pathology-specific templates.

A robust algorithm for gait cycle segmentation

“…= 0.5 is chosen for normalized data and achieves satisfactory performance [4] (for this experiment, the datasets are normalized from 0 to 1 in magnitude). But it may not always be the optimal segmentation value …”

“…As a newly developed research area, gait analysis has shown great potential for diagnosis in neurodegenerative diseases such as Parkinson's disease or peripheral neuropathy. Different systems are used in order to obtain gait signals, such as vision-based systems [1], goniometers [2], Inertial Measurement Units (IMUs) [3], foot pressure sensors [4]. Gait data are commonly recorded at different parts of human body, such as lower limbs, feet, and lower waist.…”

“…In the work presented in [4], a gait segmentation algorithm on gait signals collected from pressure sensors mounted under feet (foot-switch signals) was proposed. The starting point of each cycle was defined as the points whose difference with the subsequent point is not equal to 0, and a threshold rule was employed for gait phase segmentation.…”

Heuristic based gait event detection for human lower limb movement

“…Gait events can be detected using either force based measurement systems by means of footswitches such as force sensitive resistors (FSR) [4], or wearable sensor such as Inertia Measurement Unit (IMU) [5]. To perform outdoor activities for longer period of time, it is crucial to use the systems which are reliable, portable, small, inexpensive, and with low power consumption [6][7][8].…”

“…Patients with pathologic gait suffer from higher energy consumption and risk of falls. Gait analysis and event detection has been used in different applications using ambulatory gait systems to evaluate and improve patients' motilities and to control the functional electrical stimulation (FES) [1][2][3].Gait events can be detected using either force based measurement systems by means of footswitches such as force sensitive resistors (FSR) [4], or wearable sensor such as Inertia Measurement Unit (IMU) [5]. To perform outdoor activities for longer period of time, it is crucial to use the systems which are reliable, portable, small, inexpensive, and with low power consumption [6][7][8].…”