Real time falls prevention and detection with biofeedback monitoring solution for mobile environments

With the elderly population growing around the world, falls increase the risk progressively with age. Those falls can origin injuries that may cause a great dependence and debilitation to the elderly, and even death in extreme cases. This paper reviews the related literature about this topic and introduces a mobile solution for falls prevention, detection, and biofeedback monitoring. The falls prevention system uses collected data from sensors in order to control and advice the patient or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of prolonged symptoms it can even detect a possible disease. The signal processing algorithms play a key role in a fall prevention system. In real time, based on biofeedback data collection, these algorithms analyses bio-signals to thereby warn the user, when needed. Monitoring and processing data from sensors is performed by a smartphone that will issue warnings to the user and, in gravity situations, send them to a caretaker. The proposed solution for falls prevention and detection is evaluated and validated through a prototype and it is ready for use.