  An approach for computing unique gait signature using measurements collected
from body-worn inertial measurement units (IMUs) is proposed. The gait
signature represents one full cycle of the human gait, and is suitable for
off-line or on-line classification of the gait mode. The signature can also be
used to jointly classify the gait mode and the device mode. The device mode
identifies how the IMU-equipped device is being carried by the user. The method
is based on precise segmentation and resampling of the measured IMU signal, as
an initial step, further tuned by minimizing the variability of the obtained
signature within each gait cycle. Finally, a Fourier series expansion of the
gait signature is introduced which provides a low-dimensional feature vector
well suited for classification purposes. The proposed method is evaluated on a
large dataset involving several subjects, each one containing two different
gait modes and four different device modes. The gait signatures enable a high
classification rate for each step cycle.
