Unobtrusive monitoring and identification of fall accidents

• We present a prototype and algorithms for a fall sensor integrated in foot wear.
• This sensor can detect normal falls and typically hard to detect sliding falls.
• The algorithms are self-learning and adapt to individual users.
• Due to its integration in footwear, the sensor does not pose the risk of stigma.
• Further applications for the sensor are the measurement of general gait parameters.

Falls are a societal and economic problem of great concern with large parts of the population, in particular older citizens, at significant risk and the result of a fall often being grave. It has long been established that it is of importance to provide help to a faller soon after the event to prevent complications and this can be achieved with a fall monitor. Yet, the practical use of currently available fall monitoring solutions is limited due to accuracy, usability, cost, and, not in the least, the stigmatising effect of many solutions. This paper proposes a fall sensor concept that can be embedded in the user's footwear and discusses algorithms, software and hardware developed. Sensor performance is illustrated using results of a series of functional tests. These show that the developed sensor can be used for the accurate measurement of various mobility and gait parameters and that falls are detected accurately.