Self-assessing fall risk and motivating technologies for fall prevention

Introduction Falls among older adults result in high costs for care, rehabilitation and on personal level. The prevention of falls would lower the expenses. We will present a novel system for older adults, for preventing falls early enough. AAL - Ageing in Balance (AiB) develops the concept of fall prevention that can be implemented for the rehabilitation and gym exercises. Methods Self-assessment system is important because the demographic change in European countries produces a larger number of older adults and less care personnel. The self-assessment can be used individually, for assessing fall risks or as a supportive part of an assessment performed by multidisciplinary group of professionals. The assessment is done by filling up a questionnaire or performing certain activities such as physical or cognitive tasks. The developed automatic assessment will use information gathering from some sensors and activity monitoring. It is possible to assess the sway of the body from a simple scale with sensors. The system uses data to calculate values for a set of predefined parameters and fuses the derived information with any other available assessments to determine the fall risk of a person. If there are differences compared to normal level, the system gives an alert and precaution actions can be made, or the system can guide the user to seek into the professional assessment, if the fall risk is high. AiB will provide a risk factor framework eventually to be based on a formal ontology and typing system. Motivated people are eager to estimate their own fall risk and to reduce it. This kind of behaviour can be called preventive rehabilitation behaviour that is based on people's own will to take care of his/her physical and mental abilities. After finding out the risk factors, the system recommends what kind of actions would be needed for lowering them. Personal activation needs motivating tools and these will be developed. They are designed and built up to support maintaining or improving the muscle strength, guide balance training, review and monitor pharmacological treatments, provide psychological and cognitive exercises, to name but a few. The fall risk of the older person is regularly being assessed to verify the effectiveness of the interventions. Reminders will be used to keep the older adults active and motivated to carry on their exercises. It will be possible to share the information and achieved results with own family members, friends and health care personnel when needed. Results The older adults and professional care givers are involved in designing and specifying the developed system right from the beginning. They evaluate the proposed scenarios in focus group interviews and take part in iterative system implementation and testing. The effectiveness of the system is tested in field trials from two perspectives: 1.) system's ability to estimate multifactorial fall risk of a person and 2.) system's ability to prevent falls by measuring the change of fall risk of a person, after using the preventive system for a test period. Because the aim is to support and motivate older adults to be active and use intelligent gym equipment, some are used during the project. Conclusions This method to assess the risk for falls will be developed with older users and specialists. The system is beneficial for both interest groups in the sense of prevention and follow-up. During the phase of the project researchers have surveyed the phenomena and collected data for the state of art description. Based on the information the measurable factors will be decided. So far six (6) scenarios are written and evaluated with focus groups.