SUMMARY

This research encompasses research in linear resonant actuator (LRA) modeling, human perception, human testing experiments, control of an LRA with shapers, tuning of controls and haptic profiles with machine learning, human physiology of the nervous system and signal perception, upper-limb prosthesis with haptic feedback for sensing touch, and robotics applications to prostheses and medical devices. Accurate modeling of LRA motor’s nonlinear electromagnetic system dynamics is presented. Verification of the model is provided. Control of LRA motors using input shaping techniques is done to create meaningful and intuitive touch sensations. The current general understanding of human perception with respect to haptic touch sensation is explained based on the recent research in the field. Human testing experiments studying psychology and physiology of human perception of wrist-based haptic touch sensations include emotional reactions tests, context recognition tests, auditory distraction tests, and real-world daily task and social touch communication tests. Human physiology of the nervous system and signal perception are explored to inform the design of haptic profiles and testing experiments. The use case for upper-limb prosthesis haptics and for haptic feedback for sensing touch on the wrist are explored as direct possible applications of the research. Robotics applications for prostheses and medical devices are explored and mentioned for the direct use application techniques. Tuning of control parameters and haptic profile parameters is done using machine learning techniques. Future work will explore the design of haptic profiles for individual users to create more personalized haptic profile designs. This will create more-effective haptic profiles for social and emotional touch sensation and perception.