  Differences in gait patterns of children with Duchenne muscular dystrophy
(DMD) and typically-developing (TD) peers are visible to the eye, but
quantifications of those differences outside of the gait laboratory have been
elusive. In this work, we measured vertical, mediolateral, and anteroposterior
acceleration using a waist-worn iPhone accelerometer during ambulation across a
typical range of velocities. Fifteen TD and fifteen DMD children from 3-16
years of age underwent eight walking/running activities, including five 25
meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1
to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run
(100MRW), and a free walk (FW). For clinical anchoring purposes, participants
completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial
gait clinical features (CFs) and applied multiple machine learning (ML)
approaches to differentiate between DMD and TD children using extracted
temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed
reduced step length and a greater mediolateral component of total power (TP)
consistent with shorter strides and Trendelenberg-like gait commonly observed
in DMD. ML approaches using temporospatial gait CFs and raw data varied in
effectiveness at differentiating between DMD and TD controls at different
speeds, with an accuracy of up to 100%. We demonstrate that by using ML with
accelerometer data from a consumer-grade smartphone, we can capture
DMD-associated gait characteristics in toddlers to teens.
