Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Long lies after a fall remain a public health challenge. Many successful fall prevention programmes have been developed but only few of them include recovery strategies after a fall. Once better understood, such movement strategies could be implemented into training interventions. A model of motion sequences describing successful movement strategies for rising from the floor in different age groups was developed. Possible risk factors for poor rising performance such as flexibility and muscle power were evaluated. Fourteen younger subjects between 20 and 50 years of age and 10 healthy older subjects (60+ years) were included. Movement strategies and key components of different rising sequences were determined from video analyses. The temporal parameters of transfers and number of components within the motion sequences were calculated. Possible explanatory variables for differences in rising performance were assessed (leg extension power, flexibility of the knee- and hip joints). Seven different components were identified for the lie-to-stand-walk transfer, labelled as lying, initiation, positioning, supporting, elevation, or stabilisation component followed by standing and/or walking. Median time to rise was significantly longer in older subjects (older 5.7s vs. younger 3.7s; p < 0.001), and leg extension power (left p = 0.002, right p = 0.013) and knee flexibility (left p = 0.019, right p = 0.025) were significantly lower. The number of components for rising was correlated with hip flexibility (r = 0.514) and maximal power (r = 0.582). The time to rise was correlated with minimal goniometric knee angle of the less flexible leg (r = 0.527) and maximal leg extension power (r = 0.725). A motion sequence model containing seven different components identified by individual key-frames could be established. Age-related differences in rising strategies and performance were identified.
Diego Felipe Paez Granados, Yang Chen
,