Statistical Analysis of the Effectiveness of Wearable Robot

Abstract

In this paper, we present a new statistical approach for evaluating the time-dependent effectiveness of wearable robots without real work. In total, 10 subjects participated in three phases of the experiment; not equipped with a wearable robot without any load, not equipped with the wearable robot with a 15 kg load, equipped with the wearable robot with a 15 kg load. A higher limb wearable robot called LEXO-W was utilized. We measured the time taken to complete a 10 m round trip 10 times as a lap time, and each participant was measured multiple times under all conditions. An increasing number of round trips causes an increment in lap times. In particular, the load-carrying group showed a rapid upward trend in lap time over the number of round trips. However, the robot-assisted group showed a slightly upward trend of lap time over the number of round trips. This study statistically shows that the LEXO-W helps reduce physical fatigue by using repeated measure ANOVA analysis. Furthermore, we employed the generalized additive model(GAM) model to predict and evaluate the effectiveness of the wearable robot.