  Accelerometer signals generated through gait present a new frontier of human
interface with mobile devices. Gait cycle detection based on these signals has
applications in various areas, including authentication, health monitoring, and
activity detection. Template-based studies focus on how the entire gait cycle
represents walking patterns, but these are compute-intensive. Aggregate
feature-based studies extract features in the time domain and frequency domain
from the entire gait cycle to reduce the number of features. However, these
methods may miss critical structural information needed to appropriately
represent the intricacies of walking patterns. To the best of our knowledge, no
study has formally proposed a structure to capture variations within gait
cycles or phases from accelerometer readings. We propose a new structure named
the PQRST Complex, which corresponds to the swing phase in a gait cycle and
matches the foot movements during this phase, thus capturing the changes in
foot position. In our experiments, based on the nine features derived from this
structure, the accelerometer-based gait authentication system outperforms many
state-of-the-art gait cycle-based authentication systems. Our work opens up a
new paradigm of capturing the structure of gait and opens multiple areas of
research and practice using gait analogous to the "QRS complex" structure of
ECG signals related to the heart.
