Detection of human impacts by an adaptive energy-based anisotropic algorithm.

Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers--unobtrusiveness and reliability--defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions.

The inescapable smart impact detection system (ISIS): An ubiquitous and personalized fall detector based on a distributed 'divide and conquer strategy'.

Despite the intense research in the last decade with the aim of developing a reliable solution for fall detection in the elderly and other risk populations, it can be asserted that the diffusion of fall detectors in the geriatric practice is near null. This scenario is similar to the very scarce use of telemedicine in healthcare. The present work begins analyzing why fall detectors have not achieved to permeate the industry.