###### Abstract  
Up to 30% of people over the age of 60 are at high risk of falling, which can cause injury, aggravation of pre-existing conditions, or even death, with up to 684,000 fatal falls reported annually. This is due to the difficult task of establishing a preventive system for the care of the elderly, both in the hospital environment and at home. Therefore, this work proposes the development of an intelligent vision system that uses a novel methodology to infer fall risk from the analysis of kinetic and spatiotemporal gait parameters. In general, each patient is assessed using the Tinetti scale. Then, the computer vision system estimates the biomechanics of walking and obtains gait features, such as stride length, cadence, period, and range of motion. Subsequently, this information serves as input to an artificial neural network that diagnoses the risk of falling. Ninety-six participants took part in the study. The system's performance was 99.1% accuracy, 94.4% precision, 96.9% recall, 99.4% specificity, and 95.5% F1-Score. Thus, the proposed system can evaluate the fall risk assessment, which could benefit clinics, hospitals, and even homes by allowing them to assess in real time whether a person is at high risk of falling to provide timely assistance. fall risk; machine learning; gait assessment; computer vision; physiotherapy; biomechanics; elderly +
Footnote †: journal: Medical  
plex and unstructured information [7]. This has facilitated the processing of large amounts of information from images, text, and sound, the analysis of which is not feasible with traditional methods due to ambiguities in the information [8]. Based on this, intelligent systems have been developed to provide fall risk information, classified according to the type of sensor used and the function of the system [9]. Commonly used sensors can be divided into three categories: wearable sensors, computer vision systems, and environmental or fusion sensors [10]. Wearable sensor systems typically consist of accelerometers [11; 12; 13], gyroscopes, or a combination of both [14]. These are referred to as inertial measurement units (IMUs) and are placed directly on the user to provide continuous individual monitoring, which, in many cases, allows free movement [10]. [PERSON] et al. [15] and [PERSON] et al. [16] achieved promising results by developing systems with an accuracy of 99% for fall detection, which alerts the hospital or clinic staff. However, these have the disadvantage that patients may forget to use the device or recharge their batteries [17]. Alternatively, there are computer vision systems that operate in a predetermined area, such as a hallway, room, or garden, among others, to monitor one or more people within the field of view (FOV), which is continuous and non-invasive [9]. This is carried out using deep learning APIs for image processing, such as Mediapipe [18] or YOLO [8], which have functions for human detection, face recognition, and skeleton reconstruction, to name a few. Using these tools, fall risk assessment systems have been developed, such as the work of [PERSON] et al. [19] and [PERSON] et al. [20], where vision systems evaluate established tests for calculating fall risk, such as the Tinetti tests and the Berg Balance Scale (BBS), allowing patients to be classified into low, medium, and high risk categories, with an accuracy of up to 97%. Similarly, [PERSON] and [PERSON] [17] used a vision system to recognize people and their poses using MobileNet to perform a binary classification between a fall and non-fall state, with a maximum accuracy of 100%. Finally, environmental, or fusion systems tend to use vibration sensors [21], microphones [22], and pressure plates [10]. These are used to characterize the signals generated during the fall event; however, they do not have any type of feedback that allows them to distinguish between the fall of a person or an object. This makes them very practical systems to implement but with many risks of false alarms [10].