  Wearable technology for the automatic detection of gait events has recently
gained growing interest, enabling advanced analyses that were previously
limited to specialist centres and equipment (e.g., instrumented walkway). In
this study, we present a novel method based on dilated convolutions for an
accurate detection of gait events (initial and final foot contacts) from
wearable inertial sensors. A rich dataset has been used to validate the method,
featuring 71 people with Parkinson's disease (PD) and 67 healthy control
subjects. Multiple sensors have been considered, one located on the fifth
lumbar vertebrae and two on the ankles. The aims of this study were: (i) to
apply deep learning (DL) techniques on wearable sensor data for gait
segmentation and quantification in older adults and in people with PD; (ii) to
validate the proposed technique for measuring gait against traditional gold
standard laboratory reference and a widely used algorithm based on wavelet
transforms (WT); (iii) to assess the performance of DL methods in assessing
high-level gait characteristics, with focus on stride, stance and swing related
features. The results showed a high reliability of the proposed approach, which
achieves temporal errors considerably smaller than WT, in particular for the
detection of final contacts, with an inter-quartile range below 70 ms in the
worst case. This study showes encouraging results, and paves the road for
further research, addressing the effectiveness and the generalization of
data-driven learning systems for accurate event detection in challenging
conditions.
