For older adults, falling accidents can be life-changing events. They may lead to loss of autonomy (necessitating people to move to a nursing home, for instance) – but psychological side effects are not to be neglected either (fear of falling might lead to avoidance behavior). Accurate fall detection is a hot topic. FallRisk therefore investigated how to improve existing fall detection systems and develop an intelligent framework to assess the fall risk of older adults.
Falling is the second leading cause of accidental death worldwide. Research has shown that more than half of the older adults living in a nursing home – and about one third of the older adults living at home – fall at least once a year, resulting in severe injuries in 10 to 15% of all cases. The FallRisk team aimed at limiting the personal and societal burden of falling accidents to an absolute minimum, while balancing the involvement of (scarce) formal and informal caregivers.
- Sensor fusion as the optimal way to detect falling accidents and risks
- Developing an intelligent backbone that captures and interprets all data from the sensor network, and that intelligently distributes information to formal and informal caregivers
- Conducting user research is key to securing user buy-in and generating useful feedback