Falls in the elderly population have become a public health problem worldwide, since they represent one of the main causes of disability. Automatic identification of the possible danger of falls would help prevent them before they happen. In this research, an alternative approach is proposed as an automated solution, based on the continuous monitoring of the person for a day, as a "Holter" type recording of movement patterns along with the correlation of the intrinsic and extrinsic factors that predispose to a greater risk of falling. The crossing of all the variables recorded and associated with the risk of falls will allow better preventive decisions to be made against them, reducing their morbidity and, at the same time, the costs and burden of the associated health services. It is proposed to design and implement a smartphone application as a scientific and technological solution that allows solving the problem of estimating the risk of falls in older adults.