Micromanipulation refers to the manipulation under a microscope in order to
perform delicate procedures. It is difficult for humans to manipulate objects
accurately under a microscope due to tremor and imperfect perception, limiting
performance. This project seeks to understand factors affecting accuracy in
micromanipulation, and to propose strategies for learning improving accuracy.
Psychomotor experiments were conducted using computer-controlled setups to
determine how various feedback modalities and learning methods can influence
micromanipulation performance. In a first experiment, static and motion accuracy
of surgeons, medical students and non-medical students under different
magniification levels and grip force settings were compared. A second experiment
investigated whether the non-dominant hand placed close to the target can contribute
to accurate pointing of the dominant hand. A third experiment tested a
training strategy for micromanipulation using unstable dynamics to magnify motion
error, a strategy shown to be decreasing deviation in large arm movements.
Two virtual reality (VR) modules were then developed to train needle grasping
and needle insertion tasks, two primitive tasks in a microsurgery suturing
procedure. The modules provided the trainee with a visual display in stereoscopic
view and information on their grip, tool position and angles. Using the
VR module, a study examining effects of visual cues was conducted to train tool
orientation. Results from these studies suggested that it is possible to learn and
improve accuracy in micromanipulation using appropriate sensorimotor feedback
and training
