Gregory J. Gerling, Ph.D.

    Associate Professor

    Systems and Information Engineering

    gregory-gerling virginia edu

Greg Gerling     Gerling Lab Logo          

Welcome! Our two research areas are related to the fields of haptics, human-machine interaction, and computational neuroscience:

Computational models of tactile mechanotransduction

We are using computational models and artificial sensor correlates to understand the neural basis of touch and capture the neural behavior of the skin-receptor interaction.  We collaborate with neurophysiologists and surgeons to acquire electrophysiological and skin mechanics measurements.  Specific modeling and analysis methodologies include solid mechanics (finite element models), statistics (response surface methodology for model fitting, ANCOVA, ANOVA, design of experiments, and logistic regression) and differential equations (models of neurons and receptor transduction), and psychophysical experimental techniques (signal detection theory, methods and laws of Fechner/Weber and Stevens).  Our group has built some of the first models to combine the skin mechanics and neural dynamics for the SAI.  Our models and artificial sensor correlates are critical for engineering the signaling of artificial sensors that interface directly with the human nervous system and restore touch sensitivity (e.g., in burn victims and amputees), as well as for applications in human-robotic manipulation in medicine.
Medical simulator design and testing
The work to understand the science of tactile perception is applied in the design of simulators.  We are currently working with a group of clinicians and medical and nursing educators to create human-machine interfaces to train health care practitioners.  Specifically, we are designing, building, and evaluating physical-computerized and virtual reality simulators.  These simulators seek to train clinical palpation skills in breast and prostate screening exams and to train cognitive skills in other exams, such as chest tube insertion. The goal is to ensure that clinicians’ skills are systematically trained, time-effective and highly accurate.  The general methodologies used are task and work domain analysis, design of human-subjects experiments, systems modeling and statistical analysis, materials characterization, and simulator prototype construction with customized electronics, computer programming, silicone-elastomers, and metals.