  Just like in humans vision plays a fundamental role in guiding adaptive
locomotion, when designing the control strategy for a walking assistive
technology, Computer Vision may bring substantial improvements when performing
an environment-based assistance modulation. In this work, we developed a hip
exosuit controller able to distinguish among three different walking terrains
through the use of an RGB camera and to adapt the assistance accordingly. The
system was tested with seven healthy participants walking throughout an
overground path comprising of staircases and level ground. Subjects performed
the task with the exosuit disabled (Exo Off), constant assistance profile
(Vision Off ), and with assistance modulation (Vision On). Our results showed
that the controller was able to promptly classify in real-time the path in
front of the user with an overall accuracy per class above the 85%, and to
perform assistance modulation accordingly. Evaluation related to the effects on
the user showed that Vision On was able to outperform the other two conditions:
we obtained significantly higher metabolic savings than Exo Off, with a peak of
about -20% when climbing up the staircase and about -16% in the overall path,
and than Vision Off when ascending or descending stairs. Such advancements in
the field may yield to a step forward for the exploitation of lightweight
walking assistive technologies in real-life scenarios.
