Scientists come together to better understand patented innovations available for gene therapy
In the study, accelerometers were placed on participants in five locations: both forearms, both leg flanks and the core of the body. As participants walked, time series data was collected to create a core-limb coupling coefficient, which compares core and limb data, and a homolateral-limb coupling coefficient, which compares data from the forearm and leg flank. Combined, these provide a picture of the degree of coupling throughout the whole body.
The study focuses on human walking as a system of subsystems -- linked-up body parts that operate cooperatively in a nonlinear complex system. That means linear dynamics in statistical analysis do not describe it well, and nonlinear dynamics models are better suited to the task.
The authors use phase space reconstruction to capture the dynamics in the complex system of the human gait pattern. Their work is based upon Takens' embedding theory, which allows them to extract the embedding dimension in a one-dimensional time series dataset. The result is a data-infused methodology for screening for Duchenne muscular dystrophy.
The authors plan to continue their work to improve the ability of the relational coupling coefficient to increase its accuracy as a tool for diagnosis, as well as developing applications for elderly people, such as predicting fall risk. Source: Journal reference:
An, J., et al. (2020) Quantitative coordination evaluation for screening children with Duchenne muscular dystrophy. Chaos . doi.org/10.1063/1.5126116 .
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