###### Abstract
These intelligent systems can also be classified according to their performance. One is falling detection, such as the works of [PERSON] et al. [13], [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [23], and [PERSON] and [PERSON] [17], where a binary classification is used to determine whether the patient or elderly person has fallen, and some even notify nurses, physiotherapists, caretakers, or family members. However, while such systems typically have accuracies of around 96% and avoid long waits for assistance [24; 25], they have the disadvantage of post-fall alarms [26; 27]. This means that the patient or elderly person is already at risk of having suffered some kind of trauma caused by the fall, which may aggravate their already fragile state. To avoid this, other systems such as those by [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [28], [PERSON] et al. [29], and [PERSON] et al. [30] have opted for a preventive approach that assesses fall risk and determines which patients require further assistance and continuous monitoring. However, these are designed to assist in the assessment of tests such as the BSS, Tinetti, and Morse Fall Scale, to name a few. The problem is that the assessment requires tests that are not commonly performed in a clinic or hospital due to their time-consuming nature. Instead, an approach such as that of [PERSON] et al. [28] may be more useful, as it is an analysis of fall risk based on parameters extracted from gait. In their case, a Vicon-based system and wavelet transform post-processing are used to classify patients as low or high fall risk without the need to perform specific tests, using the minimum foot clearance (MFC) as a gait feature. This type of system offers the advantage of continuous, non-invasive, and efficient assessment in terms of time management, but there is still room for improvement when considering that only the MFC is assessed. This is a measurement that can be difficult to obtain in an uncontrolled environment, as the visualization of the feet can be easily obstructed. On the other hand, the gait has other indicators that could have a better performance and can be obtained more easily, such as stride length, cadence, speed, period between steps, and knee movement ranges, among others. This paper proposes a new methodology to establish a relationship between fall risk and parameters extracted from gait, using Tinetti test scores as ground truth, to develop a monitoring system that can continuously assess fall risk without interfering with the activities of patients or professionals. This is expected to establish a relationship between gait and fall risk that can be used by an intelligent fall risk assessment system in a clinic or hospital to keep staff aware of the status of their patients. Therefore, the main contributions of this work are an automated methodology to estimate the biomechanics of walking, obtaining gait characteristics, and assessing fall risk; and a low-cost computer vision system that includes an automated fall risk assessment methodology with performance indicators of 99.1% accuracy, 94.4% precision, 96.9% recall, 99.4% specificity, and 95.5% F1-Score.