TY - GEN
T1 - Towards Telementoring for Needle Insertion
T2 - 27th IEEE Haptics Symposium, HAPTICS 2022
AU - Reyes, Lourdes R.
AU - Gavino, Phillip
AU - Zheng, Yi
AU - Boehm, Jacob
AU - Yeatman, Mark
AU - Hegde, Shruti
AU - Park, Caroline
AU - Battaglia, Edoardo
AU - Fey, Ann Majewicz
N1 - Funding Information:
This research is supported by MEXT/JSPS Kakenhi 17H04780, 19K21701, 21H05069, 21H05070 and JST Moonshot R&D Program JPMJMS2013
Funding Information:
1Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA lourdes.reyes@austin.utexas.edu 2Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA 3Department of Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA ∗The first and second authors contributed equally to this work. This work was supported in part by NSF award #2102250 and an REU supplement. This work was also supported in part by NSF Award #2109635.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85130619612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130619612&partnerID=8YFLogxK
U2 - 10.1109/HAPTICS52432.2022.9765597
DO - 10.1109/HAPTICS52432.2022.9765597
M3 - Conference contribution
AN - SCOPUS:85130619612
T3 - IEEE Haptics Symposium, HAPTICS
BT - 2022 IEEE Haptics Symposium, HAPTICS 2022
PB - IEEE Computer Society
Y2 - 21 March 2022 through 24 March 2022
ER -