# A Virtual Reality-Based Simulation Tool for Assessing the Risk of Falls in Older Adults
## 2 Related Work  
The fall risk classification can be defined as a single or group of assessments performed to evaluate the risk of falls to individuals and to provide feedback if follow-up assessments or training are required. The standard methods are followed based on the individual's level of risk of falls to customize or implement assessments and interventions [20]. Some common fall risk classification methods include self-reported questionnaires, physical exercise tests, and posturography procedures. These methods have some limitations, advantages, and drawbacks. For example, the Stay Independent Brochure is a valid and reliable instrument for the risk assessment of falls [17]. However, it can only be used for certain populations and takes longer to complete. The literature also highlighted other physical functional tests, such as the timed-up-and-go (TUG) test, and the Berg balance scale or walking speed, as profiling tools [20]. However, these mobility tests require trained healthcare professionals to evaluate the risk of falls in healthy community-dwelling older adults [18]. Computer-based posturography is another way to assess the individual's balance and quantify body sway. The posturographic parameters can be acquired for static and dynamic balance conditions, providing valuable information on postural controls [19]. Multivariate logistic regression may not be feasible to obtain the optimal fall risk classification as the posturographic parameters are highly correlated and may be non-linear. Therefore, utilizing artificial intelligence (AI)-based methods such as machine learning may solve the complexity of the data set [21]. AI-based methods may still require trained professionals, but the goal of AI-based methods may increase the autonomy of those professionals and help to solve the complexity of the data set generated during the tests. Posturographic parameters and AI-based methods have been used in several studies for multiple types of elderly populations belonging to different groups or organizations [22; 23; 24; 25; 26] such as osteoporoptic [27], parkinsonism [28], and multiple sclerosis [29]. The posturography systems comprise multiple hardware components such as force platforms [25; 28; 29], pressure platforms [24], inertial sensors [24; 30], or depth cameras [26] to acquire posturographic data. The common machine learning algorithms are mostly used in studies of random forests, decision trees, neural networks, support vector machines (SVMs), and k-nearest neighbors [24; 25; 29]. The receiver's operating characteristic (ROC) analysis [24] shows that these algorithms can reach accuracy between 80 and 99.9% [25; 27; 29; 31] or an area under the curve (AUC) between 85 and 88%. Some studies evaluated the efficacy of wearable sensor-based functional assessments for predicting the risk of falls. The machine learning models were implemented to classify participants as fallers (Fs) and non-fallers (NFs), based on the features of the sensor data. The following criteria were used to classify participants as Fs and NFs: quantitative evaluation of the standard function procedure (e.g., Berg Balance Scale (BBS) and Tinetti Gait and Balance Scale), self-reported fall incidents, and hospitalization history [32; 33; 34]. Wearable sensor-based tools have been developed recently and are available commercially to assess the risk of falls in older adults. It has been considered that older people have low motivation for traditional physical exercise programs [35]. However, combining immersive VR (IVR) and physical exercise could be a suitable training program for older people's physical needs and requirements [36]. The literature is scarce on VR simulations to assess the risk of falls. However, most of the studies used VR-based exergames through interventions to reduce the risk of falls. Exergames are game-based physical training performed in a virtual environment and have been shown to improve physical fitness [37] and as a treatment option for multiple types of unhealthy subjects [38; 39].