The purpose of this study is to evaluate the sensing accuracy of gait variables measurable by Bot Fit and to develop a scoring system that assesses how energetic a user's gait is with an exercise perspective based on the sensed indicator and validate the energetic gait index (EGI).
All gait-related variables were identified that can be measured using the embedded sensors of Bot Fit. Subsequently, the selected variables were validated using the G-Walk, with 44 participants straight walking data conducting Intraclass Correlation Coefficient (ICC) verification. After verifying the sensing accuracy, 10 experts were selected to assign weights to each variable using the Analytic Hierarchy Process (AHP), and mathematical methods were employed to create a normal distribution curve and derive a scoring formula with discriminative power. To ensure that this scoring formula accurately reflects an energetic walking state, 106 straight walking datasets were analyzed by comparing them with walking videos.
Five gait-related variables were selected for sensing in Bot Fit: walking speed, step length, step symmetry, pelvic movement, and swing phase ratio. The ICC values for the sensing accuracy of these variables were as follows: walking speed 0.891, step length 0.714, swing phase ratio 0.661, and pelvic movement as tilt 0.057, obliquity 0.402, and rotation 0.714. The weights assigned for calculating the score were as follows: speed 20%, step length 17%, symmetry 26%, pelvic movement 18% (tilt 19%, obliquity 18%, and rotation 63%), and swing phase ratio at 19%.The mathematical scoring formula derived from the data of these variables achieved normalization and standardization among the five variables, demonstrating discriminative power. Comparisons between actual walking and the derived scores confirmed that the scoring system accurately reflects an energetic walking state.
The wearable robot Bot Fit utilizes its built-in sensors to sense five gait-related variables in real-time with high accuracy. By employing these variables, the standardized and validated EGI indicates how energetic the user's walking is. In future research, EGI derived from this study can be utilized to observe the long-term effects of actual walking exercises. Additionally, it will be possible to categorize various participant cases to determine whether there are changes in postural alignment.
EGI can provide personalized feedback during walking exercise, maximizing the effectiveness of the workout. Furthermore, it offers significant value by allowing for a straightforward evaluation of vitality in walking without the need for separate gait analysis equipment.
Gait
Scoring Systems
