  The prevalence of mobility impairments due to conditions such as spinal cord
injuries, strokes, and degenerative diseases is on the rise globally.
Lower-limb exoskeletons have been increasingly recognized as a viable solution
for enhancing mobility and rehabilitation for individuals with such
impairments. However, existing exoskeleton control systems often suffer from
limitations such as latency, lack of adaptability, and computational
inefficiency. To address these challenges, this paper introduces a novel online
adversarial learning architecture integrated with edge computing for high-level
lower-limb exoskeleton control. In the proposed architecture, sensor data from
the user is processed in real-time through edge computing nodes, which then
interact with an online adversarial learning model. This model adapts to the
user's specific needs and controls the exoskeleton with minimal latency.
Experimental evaluations demonstrate significant improvements in control
accuracy and adaptability, as well as enhanced quality-of-service (QoS)
metrics. These findings indicate that the integration of online adversarial
learning with edge computing offers a robust and efficient approach for the
next generation of lower-limb exoskeleton control systems.
