  Human identification is one of the most common and critical tasks for
condition monitoring, human-machine interaction, and providing assistive
services in smart environments. Recently, human gait has gained new attention
as a biometric for identification to achieve contactless identification from a
distance robust to physical appearances. However, an important aspect of gait
identification through wearables and image-based systems alike is accurate
identification when limited information is available, for example, when only a
fraction of the whole gait cycle or only a part of the subject body is visible.
In this paper, we present a gait identification technique based on temporal and
descriptive statistic parameters of different gait phases as the features and
we investigate the performance of using only single gait phases for the
identification task using a minimum number of sensors. It was shown that it is
possible to achieve high accuracy of over 95.5 percent by monitoring a single
phase of the whole gait cycle through only a single sensor. It was also shown
that the proposed methodology could be used to achieve 100 percent
identification accuracy when the whole gait cycle was monitored through pelvis
and foot sensors combined. The ANN was found to be more robust to fewer data
features compared to SVM and was concluded as the best machine algorithm for
the purpose.
