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

Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1123 - 1123

Published: Feb. 8, 2024

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

Language: Английский

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Wearable sensors in paediatric neurology

Developmental Medicine & Child Neurology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Wearable sensors have the potential to transform diagnosis, monitoring, and management of children who neurological conditions. Traditional methods for assessing disorders rely on clinical scales subjective measures. The snapshot disease progression at a particular time point, lack cooperation by during assessments, susceptibility bias limit utility these sensors, which capture data continuously in natural settings, offer non-invasive objective alternative traditional methods. This review examines role wearable various paediatric conditions, including cerebral palsy, epilepsy, autism spectrum disorder, attention-deficit/hyperactivity as well Rett syndrome, Down Angelman Prader-Willi neuromuscular such Duchenne muscular dystrophy spinal atrophy, ataxia, Gaucher disease, headaches, sleep disorders. highlights their application tracking motor function, seizure activity, daily movement patterns gain insights into therapeutic response. Although challenges related population size, compliance, ethics, regulatory approval remain, technology promises improve trials outcomes patients neurology.

Language: Английский

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Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Digital outcome measures in Duchene muscular dystrophy: Lessons learnt from clinical trials

Journal of Neuromuscular Diseases, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 8, 2024

Duchenne muscular dystrophy is a severe neuromuscular disorder characterized by progressive muscle degeneration resulting from mutations in the dystrophin gene. Digital outcome measures offer promising alternative to traditional used clinical trials. This review explores development and application of digital dystrophy, emphasizing feasibility, reliability, sensitivity, validity these measures. The stride velocity 95th centile has been validated as robust endpoint approved for use evaluation drugs treatment European Medicines Agency. Although have potential enhance efficiency accuracy trials, challenges such limited sample sizes patient compliance persist. integration artificial intelligence into data analysis progress, but further validation required before strategies can be incorporated future trial methodologies.

Language: Английский

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