Towards Telementoring for Needle Insertion: Effects of Haptic and Visual Feedback on Mentor Perception of Trainee Forces

• Published in: 2022 IEEE Haptics Symposium (HAPTICS) pp. 1 - 7
• Main Authors: Reyes, Lourdes R., Gavino, Phillip, Zheng, Yi, Boehm, Jacob, Yeatman, Mark, Hegde, Shruti, Park, Caroline, Battaglia, Edoardo, Fey, Ann Majewicz
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.03.2022
• Subjects: Force; Lung; Needles; Surgery; Three-dimensional displays; Training; Visualization
• ISSN: 2324-7355
• DOI: 10.1109/HAPTICS52432.2022.9765597

Emergency needle decompression is a life-saving procedure performed to treat patients with air trapped between the chest wall and the lungs. This condition can severely compromise heart and lung function, almost always leading to death if untreated. However, the needle decompression task itself carries many potential risks and complications due to proximity to vital organs, particularly if the operator is not sufficiently trained. In this paper, we present a device to help facilitate needle decompression training in which a mentor can feel the needle insertion forces exerted by the trainee. We developed a custom 3D printed attachment for decompression needle with an embedded force sensor to relay axial force data from the needle to a Geomagic Touch haptic device. In our envisioned telementoring system, a remote expert will also be able to provide haptic cues related to needle guidance to the trainee. The main goal for this work is to evaluate the most effective form of visual, haptic, or combined feedback provided to the mentor on applied forces by the trainee. In our experiment, 15 subjects were recruited to act as mentors and reported their perception of needle insertion forces that were controlled by the experimenter (acting as a trainee) at three levels of force, and five feedback conditions. Results of the experiment yielded best performance in terms of accuracy for a combination of both graphic and haptic cues, with a median accuracy of 100% at correctly predicting the trainee applied force level.