Ismail, Hafsa

Maintaining a high standard of life, especially for the elderly, enables them to live their lives withminimum fear of sudden accidents. Falling is one of the most important problems that affect elderlylives. 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-carefor elderly people. While the main causes of falling are related to postural sway and walking, determiningabnormalities in one or both of these activities can be informative in predicting the likelihood of havinga fall. A need exists for a gait and postural sway analysis system that is easy to use, readily available,and inexpensiveThis PhD thesis investigates using vision data, as an inexpensive option, to estimate gait and swaymovements from video recordings. It also investigates measuring human gait and postural sway parametersfrom the estimated movements within acceptable accuracy when compared to the gold standardmeasurements, Vicon and force plate. Furthermore, based on analysing the measured gait and swayparameters 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 deteriorationin 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 toavoid possible fall accidents well before they happen. Seeking help and including proper exercises in thedaily 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, elderlypeople over fifty years and younger healthy athletes. The dataset part for the healthy athletes group ofpeople is considered as the ground truth part. Vision data is the main data type in the two parts. Theground truth part of the dataset contains centre of pressure displacements from a force plate as well asthe 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 datais proposed and validated. Another model to estimate human gait from vision data is also built andvalidated using the ground truth part of the dataset. The estimated gait and sway movements are used tomeasure defined parameters that are used to analyse the gait and sway. The two computational modelsachieved high correlation between the estimated gait and sway signals from video recordings comparedto 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 thegroup of elderly people, then the gait and the sway parameters are measured. Based on the measuredsway parameters, each elderly subject is assigned to a sway-age group. Also, for the measured gaitparameters, each elderly subject is assigned to a gait-age group. The chronological age of the subjects iscompared 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 withan acceptable accuracy that has reached 90% for the estimated postural sway and gait referenced tothe measurements computed from the ground truth data. The proposed methods are then used on theelderly people data to estimate their sway and gait movements. Using these estimations, sway andgait parameters are measured. From these parameters, each elderly subject is clustered into gait/swayage group. Comparing subject’s gait/sway age group with his/her chronological age group defines alikelihood risk of having a fall that gives the opportunity to intervene to improve maintaining the balanceto avoid fall accidents.<br/

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A computational model for gait and postural sway for the elderly

https://researchsystem.canberra.edu.au/ws/files/45329882/Hafsa_Ismail.pdf

A Computational Model for Gait and Postural Sway for the Elderly

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