Toward Correcting Anxious Movements Using Haptic Cues on the Da Vinci Surgical Robot.

Surgical movements have an important stylistic quality that individuals without formal surgical training can use to identify expertise. In our prior work, we sought to characterize quantitative metrics associated with surgical style and developed a near-real-time detection framework for stylistic deficiencies using a commercial haptic device. In this paper, we implement bimanual stylistic detection on the da Vinci Research Kit (dVRK) and focus on one stylistic deficiency, "Anxious", which may describe movements under stressful conditions. Our goal is to potentially correct these "Anxious" movements by exploring the effects of three different types of haptic cues (time-variant spring, damper, and spring-damper feedback) on performance during a basic surgical training task using the da Vinci Research Kit (dVRK). Eight subjects were recruited to complete peg transfer tasks using a randomized order of haptic cues and with baseline trials between each task. Overall, all cues lead to a significant improvement over baseline economy of volume and time-variant spring haptic cues lead to significant improvements in reducing the classified "Anxious" movements and also corresponded with significantly lower path length and economy of volume for the non-dominant hand. This work is the first step in evaluating our stylistic detection model on a surgical robot and could lay the groundwork for future methods to actively and adaptively reduce the negative effect of stress in the operating room.