English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background: Estimation of temporospatial clinical features of gait (CFs),such as step count and length, step duration, step frequency, gait speed anddistance traveled is an important component of community-based mobilityevaluation using wearable accelerometers. However, challenges arising fromdevice complexity and availability, cost and analytical methodology havelimited widespread application of such tools. Research Question: Canaccelerometer data from commercially-available smartphones be used to extractgait CFs across a broad range of attainable gait velocities in children withDuchenne muscular dystrophy (DMD) and typically developing controls (TDs) usingmachine learning (ML)-based methods Methods: Fifteen children with DMD and 15TDs underwent supervised clinical testing across a range of gait speeds using10 or 25m run/walk (10MRW, 25MRW), 100m run/walk (100MRW), 6-minute walk (6MWT)and free-walk (FW) evaluations while wearing a mobile phone-based accelerometerat the waist near the body's center of mass. Gait CFs were extracted from theaccelerometer data using a multi-step machine learning-based process andresults were compared to ground-truth observation data. Results: Modelpredictions vs. observed values for step counts, distance traveled, and steplength showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p<0.0001).The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) forstep counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for steplength compared to ground truth observations for the combined 6MWT, 100MRW, andFW tasks. Significance: The study findings indicate that a single accelerometerplaced near the body's center of mass can accurately measure CFs acrossdifferent gait speeds in both TD and DMD peers, suggesting that there ispotential for accurately measuring CFs in the community with consumer-levelsmartphones.

Automated Detection of Gait Events and Travel Distance Using Waist-worn Accelerometers Across a Typical Range of Walking and Running Speeds