For legged robots to operate in complex terrains, they must be robust to the
disturbances and uncertainties they encounter. This paper contributes to
enhancing robustness through the design of fall detection/prediction algorithms
that will provide sufficient lead time for corrective motions to be taken.
Falls can be caused by abrupt (fast-acting), incipient (slow-acting), or
intermittent (non-continuous) faults. Early fall detection is a challenging
task due to the masking effects of controllers (through their disturbance
attenuation actions), the inverse relationship between lead time and false
positive rates, and the temporal behavior of the faults/underlying factors. In
this paper, we propose a fall detection algorithm that is capable of detecting
both incipient and abrupt faults while maximizing lead time and meeting desired
thresholds on the false positive and negative rates