Falls are an important aspect of older people's health because they trigger major injuries and even death in one-third of fallen patients, making them a major public health problem. Given the risk of physical and psychological injury, if serious injuries occur as a result of a fall, prevention is an important consideration in today's health care landscape, where the population is predominantly adult world wide. This paper presents the applicability ofa simple technique of analysis of gait signals capturedby mobile devices with the objective to the generation of early warnings on the risk of falls in older adults, which correlates with subjective scales. The technique is tested in a population of patients showing results of the significant risk of falls inpatients that the subjective scales could not detect, demonstrating that mobile devices and signal processing can become important tools in the service of elderly care in fall risk prevention.