Moving Assistive Mobility Forward with AI

As we go through our daily routines of work, chores, errands and leisure pursuits, most of us take our mobility for granted. Conversely, many people suffer from permanent or temporary mobility issues due to neurological disorders, stroke, injury, and age-related causes. Research in the field of robotic exoskeletons has shown significant potential to provide assistive support for patients with permanent mobility constraints, as well as an effective additional tool for rehabilitation and recovery after injury.
Though the field has made great progress in the hardware and devices for these assistive technologies, there are limitations in ease of use and in the ability to move from walking to running, from flat ground to slopes and stairs, and across different terrains. Recent developments to create exoskeleton controllers that are more responsive to the user’s environment via user-based variables such as gait and slope calculations provide rapid yet imprecise outputs. More recent inquiry into data-driven improvements such as vision-based labeling and classification are extremely promising additions in the goal to develop a true synchronous user and device interface. A major hindrance to this data-driven approach is the need for burdensome mounted cameras and on-board computing to allow for real-time in use adjustments to the environmental terrain encountered.