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Differences in gait patterns of children with Duchenne muscular dystrophy(DMD) and typically-developing (TD) peers are visible to the eye, butquantifications of those differences outside of the gait laboratory have beenelusive. In this work, we measured vertical, mediolateral, and anteroposterioracceleration using a waist-worn iPhone accelerometer during ambulation across atypical range of velocities. Fifteen TD and fifteen DMD children from 3-16years of age underwent eight walking/running activities, including five 25meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1to 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, participantscompleted a Northstar Ambulatory Assessment (NSAA). We extracted temporospatialgait clinical features (CFs) and applied multiple machine learning (ML)approaches to differentiate between DMD and TD children using extractedtemporospatial gait CFs and raw data. Extracted temporospatial gait CFs showedreduced step length and a greater mediolateral component of total power (TP)consistent with shorter strides and Trendelenberg-like gait commonly observedin DMD. ML approaches using temporospatial gait CFs and raw data varied ineffectiveness at differentiating between DMD and TD controls at differentspeeds, with an accuracy of up to 100%. We demonstrate that by using ML withaccelerometer data from a consumer-grade smartphone, we can captureDMD-associated gait characteristics in toddlers to teens.

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a  Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning  Approaches