OBJECTIVE: To characterize and classify six positions and movements for individuals in a bed using the output signals of four load cell sensors. APPROACH: A bed equipped with four load cell sensors and synchronized video was used to assess the load cell response of 54 healthy individuals in prescribed positions and as they moved between positions. Stationary positions were characterized by the signals from the four load cells and the coordinates of the center of mass (CoM). Movements were characterized by the changes in load cell signals, four parameters associated with the trajectory of the CoM between the initial and final position (Euclidean distance, length of the trajectory, and the x- and y- variances), and the initial position's CoM coordinates. Classification and decision tree models were used to assess the ability of these parameters to identify specific positions or movements. MAIN RESULTS: Six positions were classified with an accuracy of 74.9% and six movements were classified with an accuracy of 79.7%. SIGNIFICANCE: This study demonstrates the feasibility of distinguishing certain positions and movements with load cell sensors. The identification of positions and movements for individuals in bed can be used as a tool in a variety of clinical settings.
OBJECTIVE: To characterize and classify six positions and movements for individuals in a bed using the output signals of four load cell sensors. APPROACH: A bed equipped with four load cell sensors and synchronized video was used to assess the load cell response of 54 healthy individuals in prescribed positions and as they moved between positions. Stationary positions were characterized by the signals from the four load cells and the coordinates of the center of mass (CoM). Movements were characterized by the changes in load cell signals, four parameters associated with the trajectory of the CoM between the initial and final position (Euclidean distance, length of the trajectory, and the x- and y- variances), and the initial position's CoM coordinates. Classification and decision tree models were used to assess the ability of these parameters to identify specific positions or movements. MAIN RESULTS: Six positions were classified with an accuracy of 74.9% and six movements were classified with an accuracy of 79.7%. SIGNIFICANCE: This study demonstrates the feasibility of distinguishing certain positions and movements with load cell sensors. The identification of positions and movements for individuals in bed can be used as a tool in a variety of clinical settings.
Authors: Danielle M Minteer; Patsy Simon; Donald P Taylor; Wenyan Jia; Yuecheng Li; Mingui Sun; J Peter Rubin Journal: Adv Wound Care (New Rochelle) Date: 2019-12-06 Impact factor: 4.730