Experimental set-up and sensory glove interface for microsurgery.
One of the most fundamental prerequisites for successful microsurgery is thoughtful and adequate training. A combination of knowledge, technical skill, and decision making directly influences the surgical outcome. This study aims to analyse microsurgical hand coordinations quantitatively and to study the utility of a sensory glove interface in identifying the hand coordination patterns during microsurgery. A sensory glove interface used to measure the angular flexion-extension movements of the major interphalangeal joints of the hand, the grasp pressure, and the relative hand movements has been developed. Experiments of two suturing techniques were conducted with five experienced microsurgeons, and microsurgery practice was examined. The custom sensory glove interface required both signal conditioning and amplification which was directly interfaced with a custom-designed LABView software code. The sensory glove interface was calibrated using regression techniques and the set-up was validated using the Bland-Altman correlation technique. The hand coordination patterns were analysed using principal-component analysis. Pareto plots showing the contribution of the principal components were analysed. The contributions of the first two sensory data components have also been compared with hand coordination studies. The temporal variations provided new insights into the underlying synergetic mechanisms and in particular the relation between different suture techniques on grasp pressure.
Amirouche
F
F
Biomechanics Research Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, USA. amirouch@uic.edu
Martin
J R
JR
Gonzalez
M
M
Fergusson
L
L
D003194:Computer-Assisted Instruction / Q000295:instrumentation* / Q000379:methods; D005932:Gloves, Surgical; D007087:Illinois; D008866:Microsurgery / Q000193:education* / Q000295:instrumentation* / Q000379:methods; D011361:Professional Competence; D025321:Surgery, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D013647:Task Performance and Analysis; D014110:Touch; D014159:Transducers; D014584:User-Computer Interface