Powered Hip Exoskeleton

Our autonomous powered hip exoskeleton augments human by providing a hip assistance across wide ranges of locomotion tasks. The device integrates human neural commands by measuring the surface electromyography (EMG) signals to control the device providing a more natural assistance to the user. We utilize these biological signals along with mechanical sensors from the device to better understand what the user intent is during different gait modes with different machine learning techniques (i.e. level-ground, stairs, and ramps). We primarily focus on translating this technology to more realistic settings such as outdoor terrain as well as to the clinical populations such as stroke survivors who experience limited mobility in community ambulation.

Graduate Student Lab Members:

  • Inseung Kang
  • Dean Molinaro
  • Pratik Kunapuli
  • Julian Park

Collaborators:

  • Dr. Geza Kogler
  • Dr. Stephen Sprigle