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Advanced Fall Detection Systems for the Older Adults


Our organisation type is:: University

We are active in the following Technologies: communication, microsystems, other, sensors-actuators, tele-monitoring

Action type: Research, Development, Studies, Other

Applications: Health & wellness, Safety, security, privacy, Home care, Other

Elderly people are generally affected by different problems such as the diminution of muscle strength, decreased balance, vision difficulties and neurodegenerative diseases, among others. Due to these problems, aged people frequently have less mobility and autonomy, as well as increased difficulties to perform normal daily activities, making them a particular group prone to suffer fall events. In average, each year, one in every three adults over 65 years older experiences a fall event and this ratio increases to one in every two adults aging more than 80 years.

Our project contribution relates to a new approach for a fall detection system. The device integrates advanced sensors used to characterize movements through selected features extracted from raw data. A classification system is then trained, based on the extracted features, to find relations between the input variables that allow to discriminate several movements from a fall. The already performed tests present low false positives and low false negatives rates with good specificity and sensitivity values, indicating that the device can be used in a real context application. Moreover, the proposed approach introduces a learning and adaptation capability to different scenarios not found in other devices with the same purpose.
The communication infrastructures are fundamental parts to broadcasting the emergency state in those cases. In this scope, several technology types can be used (e.g. GSM; TCP/IP).  A set of other functional modes can be added to enhance the device features. 
The commercial target to this device is focused on the home and the institutional (e.g. hospital) applications.

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