Human factors to develop a safety guard model in human-robot interaction
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Abstract
In this research, a strategy to incorporate an assist-as-needed scheme into human-robot collaboration during an object co-lifting and manipulation task is developed. Surface Electromyography (sEMG) signals from the upper-arm muscles are collected and analyzed to quantify fatigue while the subjects are collaborating with a Universal Robot (UR5). Rated perceived fatigue (RPF) is then utilized to quantify fatigue levels based on personal perception of the subject using the intervals of 25%, 50%, 75% and 100%. This perceived information is correlated with the change in interaction load at the object-robot interface to quantify the modifying factor needed to stay in the optimal human-robot collaboration (HRC) condition.