Individualized Motion Monitoring by Wearable Sensor : Pre-impact fall detection using SVM and sensor fusion

Abstract: Among the elderly, falling represents a major threat to the individual health, and is considered as a major source of morbidity and mortality. In Sweden alone, three elderly are lost each day in accidents related to falling. The elderly who survive the fall are likely to be suffering from decreased quality of life. As the percentage of elderly increase in the population worldwide, the need for preventive methods and tools will grow drastically in order to deal with the increasing health-care costs. This report is the result of a conceptual study where an algorithm for individualized motion monitoring and pre-impact fall detection is developed. The algorithm learns the normal state of the wearer in order to detect anomalous events such as a fall. Furthermore, this report presents the requirements and issues related to the implementation of such a system. The result of the study is presented as a comparison between the individualized system and a more generalized fall detection system. The conclusion is that the presented type of algorithm is capable of learning the user behaviour and is able to detect a fall before the user impacts the ground, with a mean lead time of 301ms.