The frequency of falls increases with age and are the leading cause of injuries in elderly people. Falling is often a result of physical, functional and cognitive impairment and it can lead to functional disabilities, loss of confidence in carrying out everyday activities, loss of independence and autonomy and even death. It is estimated that 30% of older adults aged 65 and over fall at least once a year. One in five falls result in bone fractures and the needed of medical intervention.
Fall prevention exercises have been proved to have a positive effect in reducing falls and the risk of falling. One of the most popular falls prevention exercise programmes is the Otago Exercise Programme. This programme was designed specifically to improve strength, flexibility, balance and reaction time, being the latter ones the most readily modified risk factors for falls.
Gait is one of the most import indicators of health with older adults being often exposed to falls due to gait and balance abnormalities. Reduced walking speed is a crucial factor which is strongly associated with risk of disability and falling. Gait analysis can be a valuable tool used in rehabilitation and to prevent falls. Functional gait assessment can provided valuable insight about a person gait and its deviations from normality. Usually spatial and temporal metrics such as number of steps, step length, stride width, gait speed and cadence can be extracted to describe a person gait.
The main goal of this project is to explore the use of inertial sensors to perform a full detailed functional gait assessment in specific walking exercises from Otago Exercise Programme (tandem walk, tandem walk backwards, heel walking and toe walk) . The aim is to create a gait assessment tool to work along with physiotherapists with the ultimate goal of improving balance, walking impairments and reducing risk of falling.
> Functional gait analysis of some specific exercise from Otago Exercise Programme (to be included in the scope of the FallSensing project);
> Exploring the use of state of the art balance and gait assessment metrics.
Author: Teresa Moleiro
Type: MSc thesis
Partner: Faculdade de Engenharia da Universidade do Porto