Functional Gait Assessment in Fall Prevention Exercises

The world population is ageing. Therefore, the development of solutions to help people maintain their quality of life and health is essential for contemporary societies. Older adult falls is a growing public health problem. About one-third of the older adults fall each year, and falls in the elderly population are a significant source of morbidity and mortality. Several risk factors have already been identified, and while some of them, such as age, cannot be prevented, others, such as lack of exercise, can be modified to reduce fall risk. Between the fall prevention strategies proposed in the last decades, exercise programmes showed good results in diminishing fall rates and fall risk. Gait analysis can be used to assess walking disabilities and fall risk. Gait analysis methods are usually based on observations made by specialists during walking exercises performed by the patient, but these methods lack accuracy and objectivity. Regarding objective methods, the gold standard is video-based motion capture; however it requires expensive equipment, trained personnel, specialised laboratories, and it is time-consuming. Other sensors, such as force and pressure sensors, accelerometers, gyroscopes and combinations of these, were also presented as alternatives to perform gait analysis. Most of the previously proposed gait analysis methods only evaluate free walking exercise and are not able to analyse more complex and challenging exercises that may provide more useful information to the healthcare professionals. In this dissertation, gait analysis was performed using inertial measurement units on five exercises from the Otago Exercise Programme: backwards walking, tandem walking, tandem walking backwards, heel walking and toe walking. Data were collected from a group of 14 older adults and a group of 14 younger adults. Gait parameters such as the number of strides, stride time and stride length were determined and compared to the video-recordings of the participants performing the exercise. In general, detection of strides was successful with a 4-8% relative error of non-detected strides and 1-2% of over-detected strides. Determination of the gait phase limits was achieved with a 1% relative error and the stride time had a relative error of 6%. Turn detection could be improved as the turn duration had a relative error varying between 32 and 44%. The stride length estimation was not successful with a maximum relative error of 83%. Gait parameters related to the acceleration, the angular velocity and the shank-to-vertical angle were also computed and compared between groups. Inter-group comparisons suggest that these parameters could be used to distinguish between groups. Moreover, those comparisons results also suggest that different exercises could expose different walking difficulties and, therefore, could provide more information to healthcare professionals than free walking exercises. Intraindividual comparisons of the computed gait parameters were also performed. These comparisons results also suggest that the use of different walking exercises in gait assessment could provide more information. Furthermore, intra-individual comparisons suggest that they could be used to identify impaired limbs and evaluate the success of the exercise programme.