Gait analysis is a systematic study of animal limb movements, or more precisely, the study of human walking in general. The research analysis utilizes the observer's eyes and brain, and uses instruments to assist in measuring body movement, body mechanics, and muscle activity.
Gait analysis is used to assess, plan, and treat an individual's ability to walk. It is also often used in sports biomechanics to help athletes run more efficiently, and to identify whether the casualty has posture-related or motion-related medical problems.
Gait research is a research hotspot in recent years. The current gait research laboratory mainly records the gait of the subject through a series of static motion capture cameras, which has many problems. For example, the walking distance in the laboratory is limited, so that the cameras can only record a limited number of continuous walking cycles. ; Although wearing motion sensors such as pressure-sensing insoles can record sufficient walking cycles, it cannot provide spatial information such as joint angle changes during walking.
Although the introduction of indoor gym equipment into the gait research laboratory can solve the above problems, because the treadmill uses a fixed walking speed and lacks visual flow, the subjects cannot accurately reflect their real gait when walking on the ground. Studies have shown that the self-selected walking speed (SP) of the treadmill can be achieved by installing a track with feedback control on the treadmill.
This study compared the effects of walking on the treadmill with and without visual flow on the ground walking at a self-selected walking speed. Dr. Meir Plotnik and his research team from the Gonda Brain Research Center, Bar-Ilan University, Israel A related study was conducted and published in the February 2015 electronic journal of J Neuroeng Rehabil.
All young healthy volunteers first performed a 96-meter ground walk at a comfortable pace. They were then divided into two groups. One group was given a walking speed of their choice while on the treadmill without visual flow, and the other group was given realistic visual flow while on the treadmill in a virtual reality environment. Walk at a pace of your choice. When comparing the walking speed, we mainly compared the walking conditions of four sections within a distance of 10 meters (7.5~17.5, 30.5~40.5, 55.5~65.5 and 78.5~88.5m).
The average walking speed when walking on the ground was basically equal to the optional walking speed on the final treadmill, both of which were 1.50m/s. In the absence of visual flow, the self-selected walking speed on the treadmill was less than the ground walking speed during the first and second 10-meter walk distances. The chosen walking speed on the treadmill for the third and fourth 10-meter walks was comparable to the ground walking speed.
When given a realistic visual flow in a virtual reality environment, the self-selected walking speed on the treadmill was comparable to the ground walking speed within the first 10-meter walking distance. Evaluation of curve-fitting analysis showed that when given realistic visual flow in a virtual reality environment, the self-selected walking speed on a treadmill can reach steady state in a shorter distance than without visual flow.
Also, subjects reached steady-state faster when given realistic visual flow in a virtual reality environment than without visual flow.
Therefore, this study concluded that self-paced walking on a treadmill is a reliable method for recording a typical self-paced gait, which can provide sufficient walking distance to achieve a stable walking state. Also, providing visual flow in a virtual reality situation, a steady-state walking speed can be reached in a shorter period of time, while the speed is much faster.
In addition, the researchers suggest that other gait research groups can work together to develop a standard for studying self-selected walking speeds on treadmills, such as through the use of uniform research protocols or the collection of normative data.
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