A computational model for gait and postural sway for the elderly

  • Hafsa Ismail

    Student thesis: Doctoral Thesis


    Maintaining a high standard of life, especially for the elderly, enables them to live their lives with
    minimum fear of sudden accidents. Falling is one of the most important problems that affect elderly
    lives. Falling causes injuries that may be fatal or decrease the functional ability and the quality of life.
    Predicting and preventing falls before they happen makes a critical difference, enabling optimal self-care
    for elderly people. While the main causes of falling are related to postural sway and walking, determining
    abnormalities in one or both of these activities can be informative in predicting the likelihood of having
    a fall. A need exists for a gait and postural sway analysis system that is easy to use, readily available,
    and inexpensive
    This PhD thesis investigates using vision data, as an inexpensive option, to estimate gait and sway
    movements from video recordings. It also investigates measuring human gait and postural sway parameters
    from the estimated movements within acceptable accuracy when compared to the gold standard
    measurements, Vicon and force plate. Furthermore, based on analysing the measured gait and sway
    parameters for a different age groups, an increasing risk of having a fall is predicted.
    Defining the changes in the pattern of gait and sway that are considered as signs for deterioration
    in an individual’s health status is important for predicting the risk of a fall. Monitoring these changes,
    which are continuously occurring and are inevitable while a person is aging, allows early intervention to
    avoid possible fall accidents well before they happen. Seeking help and including proper exercises in the
    daily life are examples of appropriate intervention to avoid possible fall accidents.
    First, a dataset of gait and sway activities is devised and recorded for two groups of people, elderly
    people over fifty years and younger healthy athletes. The dataset part for the healthy athletes group of
    people is considered as the ground truth part. Vision data is the main data type in the two parts. The
    ground truth part of the dataset contains centre of pressure displacements from a force plate as well as
    the joint movements in the three dimensional (3D) space from a Vicon motion capture system.
    Second, a computational learning-based model to estimate the body’s postural sway from vision data
    is proposed and validated. Another model to estimate human gait from vision data is also built and
    validated using the ground truth part of the dataset. The estimated gait and sway movements are used to
    measure defined parameters that are used to analyse the gait and sway. The two computational models
    achieved high correlation between the estimated gait and sway signals from video recordings compared
    to their corresponding signals extracted from the force plate and the Vicon system.
    Third, the gait and sway proposed models are used to estimate gait and sway movements for the
    group of elderly people, then the gait and the sway parameters are measured. Based on the measured
    sway parameters, each elderly subject is assigned to a sway-age group. Also, for the measured gait
    parameters, each elderly subject is assigned to a gait-age group. The chronological age of the subjects is
    compared with the estimated age from sway and gait movements toward analysing the fall risk.
    From the presented work, vision data can be used to estimate humans’ gait and postural sway with
    an acceptable accuracy that has reached 90% for the estimated postural sway and gait referenced to
    the measurements computed from the ground truth data. The proposed methods are then used on the
    elderly people data to estimate their sway and gait movements. Using these estimations, sway and
    gait parameters are measured. From these parameters, each elderly subject is clustered into gait/sway
    age group. Comparing subject’s gait/sway age group with his/her chronological age group defines a
    likelihood risk of having a fall that gives the opportunity to intervene to improve maintaining the balance
    to avoid fall accidents.
    Date of Award2020
    Original languageEnglish
    SupervisorRoland Goecke (Supervisor) & Gordon Waddington (Supervisor)

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