  Accurate estimation of spatial gait characteristics is critical to assess
motor impairments resulting from neurological or musculoskeletal disease.
Currently, however, methodological constraints limit clinical applicability of
state-of-the-art double integration approaches to gait patterns with a clear
zero-velocity phase. We describe a novel approach to stride length estimation
that uses deep convolutional neural networks to map stride-specific inertial
sensor data to the resulting stride length. The model is trained on a publicly
available and clinically relevant benchmark dataset consisting of 1220 strides
from 101 geriatric patients. Evaluation is done in a 10-fold cross validation
and for three different stride definitions. Even though best results are
achieved with strides defined from mid-stance to mid-stance with average
accuracy and precision of 0.01 $\pm$ 5.37 cm, performance does not strongly
depend on stride definition. The achieved precision outperforms
state-of-the-art methods evaluated on this benchmark dataset by 3.0 cm (36%).
Due to the independence of stride definition, the proposed method is not
subject to the methodological constrains that limit applicability of
state-of-the-art double integration methods. Furthermore, precision on the
benchmark dataset could be improved. With more precise mobile stride length
estimation, new insights to the progression of neurological disease or early
indications might be gained. Due to the independence of stride definition,
previously uncharted diseases in terms of mobile gait analysis can now be
investigated by re-training and applying the proposed method.
