Abstract:
A decline in balance control is a major issue for older adults as it leads to an increased risk of falling. Falls in older adults often lead to serious injury and potentially death. Injuries as a result of falls and the fear of falling affect the quality of life for older adults by preventing them from completing activities of daily living. There is also a great burden on the healthcare systems to care for older adults who have suffered falls. This issue is going to become even more pronounced as the proportion of older adults within the population rises. This thesis covers the redevelopment of a balance rehabilitation system that integrates two Nintendo Wii Balance Boards with the LabVIEW programming language as a tool to assess and improve balance in older adults. We have sought to improve all aspects of the computer program and hardware configuration from the earlier prototype with a view of benefitting older adults undergoing balance therapy, physiotherapists working in clinics and future developers of the code. We also wanted to observe the system’s ability to measure balance and to identify differences in performance of people with various conditions that affect their balance. Analysis was performed on data collected from one hundred and four participants who tested the system. The Balance Board system identified significant differences in the stepping response times between the feet of healthy people and people with conditions that affect balance. There were also significant differences in the overall stepping response times between these groups. Regression analysis identified a negative relationship between the stepping response times of people and their Berg Balance scores. In addition, a significant difference in the stepping response time between reported fallers and reported non-fallers was found. Improvements were achieved in all aspects of the performance of the Balance Board system. The research has found that there are grounds for confidence that the developed system may be a predictor of falls. This could be of great value in the early identification of those at risk of falls as it would enable preventative measures to be implemented before falls occur.
