In general, the pathological gait shows a characteristic pattern with abnormal speed and range of joint movements, such as a shortened stance phase, low walking rhythm, limited extension / flexion or inversion / eversion ankle movements. Professional doctors can easily recognize gait abnormalities and visually assess patients’ progress during physiotherapy treatments.
However, quantitative measurements allow a detailed description of these abnormalities desired for diagnostic and therapeutic use. This section first describes the system setup and ankle angles, then discusses typical modes of splitting a gait cycle, and finally the stride lengths that can be provided by pedestal gyros.
Kinematic information in biomechanical analysis is a well established set of gait parameters. To estimate spatial-temporal parameters, wearable gait analysis systems have been discussed in the literature, with two, three, four or more gyros attached to a person’s lower limbs such as the foot, shaft or thigh. Accurate orientation prediction using the gyroscope has been an important research topic in this area. For wearable systems, a reduction in the number of sensing units is highly desirable as the system will be more portable, convenient, reliable, cost effective and easy to use due to reduced overall cost and weight. Power consumption and memory requirements, time and processing required for system setup, natural motion blocking, etc.
For most types of pedal movement achieved by legged motion of humans or animals, the intuitive experience is to apply gait analysis by attaching sensors to the feet. As the foot is the part of the lower limb in the distal of the leg, it functions as the interface between the lower limb and the floor, and withstands high static and dynamic stresses that generate strong compression and shear forces, which makes the periodic nature and symptoms of disease.
The foot is more prominent than other parts of the lower limb. For example, diabetic foot is distal ankle involvement induced by various reasons due to the interaction of peripheral vasculopathy, neuropathy, and changes in foot biodynamics. For this reason, the foot is the preferred location of gyros for collecting gait data.
Such a system can give angles of ankle movements such as plantar flexion and dorsiflexion movements in the sagittal plane, and inversion and eversion movements in the coronal plane. Since ankle rehabilitation involves training range of motion in eversion and inversion, as well as plantar flexion and dorsiflexion, these movements are defined in terms of Euler angles to evaluate the ankle joint.
To analyze gait abnormalities, temporal gait parameters must first be estimated. Termally, walking is a pattern of movement that takes place during movement, exhibiting periodic patterns called gait cycles. Each gait cycle is characterized by a series of sequential walking events that occur at specific temporal locations. These events can be detected using wearable MEMS gyroscope measurements. Different researchers pay attention to different walking events according to specific application requirements. Normally, there are four typical events in a gait cycle, these are; heel strike (HS), toe flat (FF), heel closed (HO), and toe (TO) are defined as relative. It is defined on the right foot and as follows:
• HS incident: The heel hits the ground.
• FF phenomenon: The toe touches the ground and the foot becomes completely flat on the ground.
• HO event: The heel comes off the ground.
• TO event: The toe leaves the ground and the foot is fully in the air.
Usually, the HS event is specified as the beginning of a gait cycle, and a complete gait cycle is defined as the time interval between successive HS events of the same foot. Typical walking events can divide a gait cycle into two to four consecutive time intervals called gait phases. When considering more walking events, for example, mid stance and mid swing, more walking phases not covered in this section will be limited. The gait cycle section has three common modes. These;
• The first mode (1) divides a gait cycle into two phases, ie the stance phase corresponds to HS to TO and about 60% of the gait cycle.
The second mode (2) divides a gait cycle into three phases, where the stance phase is limited by HS and HO, which make up about 40% of a gait cycle.
• The third mode (3) divides a gait cycle into four phases, where the stance phase lasts from FF to HO and makes up about 30% of the gait cycle.
Obviously, in normal gait, extraordinary events are not allowed, and hence impaired gait rhythm and bilateral coordination play an important role in determining pathological gait, such as freezing of gait in Parkinson’s disease. Also, for patients with gait abnormalities, the affected lower limb cannot support body weight well, resulting in the corresponding posture phase short and a highly unstable state. Monitoring the gait cycle distribution in the transient area has been applied to detect the onset of neurodegenerative diseases and injuries.
For illustrative purposes, four typical walking events have been modeled and defined, and therefore a normalized gait cycle is divided into four phases as in the first segment mode. In the stance phase, it is the time interval when the foot is completely on the ground, the swing phase is the time interval when the foot is fully in the air, and the remaining two phases are the transitional states between stance and swing. In addition, since the movements of the subject’s two feet are strongly correlated with each other, it is expected that detecting gait events using the measurements of both feet will yield more accurate results than using the ipsilateral limb. When the relevant walking events of each foot are correctly identified, the walking cycles will be divided, and the walking phases will be limited.
When walking phases are limited, spatial gait parameters can be derived accordingly. Distance-dependent gait parameters include stride length, stride width, and stride height, corresponding to the maximum covered distance in the forward, lateral and vertical directions, respectively, along a stride. Among these three parameters, the step length in the sagittal plane must be calculated separately for each individual step. Because this varies significantly due to the inter-individual and inter-individual variability of gait. In fact, several factors may explain the phenomenon of gait variability, such as leg length, walking speed, and gait pattern. Among the literature data, stride length has different values. Average stride length is about 0.75 m for healthy adults walking at their normal speed of about 1.4 m / s of their choice, while the average stride length varies by gender, which is about 0.79 m. (0.66 m for men and women.)
One step consists of two successive steps. Both stride length and stride length are meaningful gait parameters for evaluating gait performance. Slowness of walking with reduced stride length is a manifestation of diseases that affect walking ability, such as spinal cord injury, paralysis, Parkinson’s disease, and osteoarticular disorders. Many methods have been proposed in the literature to estimate stride length and walking speed, for example using a mathematical model. Previous studies used a single inverted pendulum model to estimate stride length using a uniaxial gyroscope. It uses a pair of pendulum models consisting of an inverted pair of pendulums that rotate around the ground during stance and a pair of pendulums that rotate around the hip during oscillation.
To address the non-pendulum nature of the double limb support, a four-sensor configuration has been proposed. Typically, a gait model can be operated with various combinations of direct or indirect gyroscope measurements, with sensors attached to the person’s shaft, thigh, or lower lumbar spine near the body’s center of mass (COM). Comparisons between different stride lengths.
Based on different gait patterns, the necessary relationships between stride length and various measurable or computable gait variables can be formulated. For the dual sensor configuration, a modified gait model has been presented in previous studies conducted with measurements from standing gyros only. In this model, human walking is represented by a single inverted pendulum model with two legs without knees, taking into account the anthropometric data specific to the biomechanics of each subject.
This model functions as a self-contained stride length estimator that does not usually rely on infrared, RF or various other technologies based on ultrasonic devices using some type of beacon or active badge. Nor does it directly double integrate the gravity-compensated translational acceleration over time. The stride length SL can be estimated as the forward distance traveled by the body COM during the stance phase of the contralateral hindfoot supporting forward motion of the swinging leg.
Author: Ozlem Guvenc Agaoglu