Gait Partitioning With Smart Socks System

Abstract

Gait is a very complex movement, involving the central nervous system and a significant part of the skeletomuscular system. Any disease that is affecting one or more of the involved parts will reflect in the gait. Therefore, gait analysis has been studied extensively in the context of early disease diagnostics, post-operation rehabilitation monitoring, and sports injury prevention. Gait cycle phase partitioning is one of the most common gait characteristic analysis methods, which utilizes the cyclical nature of human gait. Pressure sensitive mats and insoles are considered the gold standard, but some inherent limitations of these methods urge researchers to seek for alternatives. One of the proposed alternatives is Smart Sock systems, which contain textile pressure sensors. The main limitation of Smart Sock systems is the limited number of sensors, thus complicating gait phase partitioning by these systems. The present paper describes gait phase partitioning using plantar pressure signal obtained by a Smart Sock system. Six-phase partitioning was achieved, including such gait phases as initial contact, loading response, mid stance, terminal stance, pre-swing and swing phase. Mean gait cycle time values obtained from the experimental data were in accordance with the ones found in the literature.

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

“…Another proposed partitioning [26] involved six gait phases, namely: initial contact, loading response, mid and terminal stances, pre-swing and swing phases. As compared to the systems with eight phases, here the swing phase could not be divided into three parts (initial, mid and terminal swing), because in order to distinguish between these three sub-phases, information about the angle between tibia and ground and of the flexion of the knee was not available.…”

“…A method for gait segmentation was also proposed by researchers from Chile [22]. As opposed to the method described in [26] who used five piezoresistive sensors, this proposal involved a number of eight capacitive sensors in each sock (therefore, a total of sixteen sensors for both feet). The positions of the sensors were chosen as in Figure 2.…”

“…In their application of gait partitioning with smart socks, the work in [26] proposed four future directions: higher sample of participants, better electronics, improved sampling rate, and the comparison of the obtained results with a reference.…”