Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
Albara Ah Ramli,
Xin Liu,
Kelly Berndt,
Erica Goude,
Jiahui Hou,
Lynea B. Kaethler,
Rex Liu,
Amanda Lopez,
Alina Nicorici,
Corey Owens,
David Rodriguez,
Jane Wang,
Huanle Zhang,
Daniel Aranki,
Craig M. McDonald,
Erik K. Henricson
Affiliations
Albara Ah Ramli
Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA
Xin Liu
Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA
Kelly Berndt
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Erica Goude
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Jiahui Hou
Department of Electrical and Computer Engineering, School of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Lynea B. Kaethler
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Rex Liu
Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA
Amanda Lopez
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Alina Nicorici
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Corey Owens
UC Davis Center for Health and Technology, University of California, Davis, CA 95616, USA
David Rodriguez
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Jane Wang
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Huanle Zhang
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Daniel Aranki
Berkeley School of Information, University of California Berkeley, Berkeley, CA 94720, USA
Craig M. McDonald
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
Erik K. Henricson
Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA
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 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m 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.
