Abstract
AbstractIn Europe 30 % of population will be aged 65 or more in 2060. Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects, as fear of falling, loss of independence and disability. Therefore, falls are a huge social and economic problem. The goals of the WIISEL project is to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk in the home setting of older adults; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. A high-tech insole with wireless communication capabilities will be worn by the elderly, monitorizing their posture and evaluating gait dynamics via a matrix of printed pressure sensors.KeywordsFall RiskGait ParameterInertial SensorFall DetectionPattern Recognition AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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