Exoskeleton Control Based on Network of Stable Heteroclinic Channels (SHC) Combined with Gaussian Mixture Models (GMM)

One of the major causes of disability and sick day leaves is lower back pain. Hence it can result in a decreased life quality and lower industrial productivity. One of the possible solutions to lower back pain could be the use of exoskeletons, which would reduce the spinal loading. One of such solutions is a quasi-passive spinal exoskeleton that engages and disengages the passive support depending on the movements performed by the user. This enables the spinal support for the user when lifting a heavy load and for all other tasks, the user motion is unobstructed. To achieve autonomous clutch activation, the main challenge is to properly classify the beginning of each motion. In this paper, we proposed a novel control method that uses Gaussian Mixture Models (GMM) for movement classifiers and a network of Stable Heteroclinic Channels (SHC) for designing a phase-state-machine. Integrating GMM into the SHC network enables a fast and reliable control of the clutch mechanism of the quasi-passive spinal exoskeleton. The control system capabilities were demonstrated in an experiment with a male subject wearing the quasi-passive exoskeleton while executing three different movements representative for an industrial working environment: walking, standing, and lifting.