Human-in-the-loop assistive cyber physical system control using physiological signals
Permanent URL:
http://hdl.handle.net/2047/D20263349
Padir, Taskin (Committee member)
Schirner, Gunar (Committee member)
In this dissertation, we present a robotic hand prosthesis control application in the HilCPS framework. The objective of this work is to develop an active hand prosthesis for people with amputated upper limbs.
First, we formulate the intent inference pipeline as a continuous grasp classification problem that can be solved with a probabilistic switched dynamical system formulation. We implement linear and non-linear models of surface EMG and compare their performance against standard processing approaches. Second, we show how context evidence in the form of mobile eye-tracking can improve grasp classification performance thus increasing theoretical system reliability. Finally, we address the problem of mapping hand grasp types to low-level joint trajectories on a simulated prosthetic hand prototype using continuous space deep reinforcement learning. We show that using a standard grasp metric as a scoring mechanism in the reward function can enable the learning of grasp motion paths from a wide range of sensor data including joint angles, RGB-D from a palm camera and contact forces.
cyber-physical systems
wheelchair navigation
tactile-based binary communication
prosthetic hand control
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