# Forward Fall Detection Using Inertial Data and Machine Learning
The paper demonstrates that insufficient sample size can be overcome by developing a digital model with the scope of extending the testing scenarios as well as the training data, using specialized software tools that are designed for human body dynamics simulation, such as Open Sim. These tools allow users to create digital models of the human body and simulate movements and interactions with the environment, including scenarios such as falls [15]. Paper [16] aims to enhance fall detection systems, which are crucial for timely medical intervention. Unlike many studies that rely on simulated falls in controlled environments, this research uses real-world fall data from the FARSEEING repository, the largest collection of real-world falls to date. Acceleration signals were recorded using an inertial sensor placed on the lower back of individuals with a moderate-to-high risk of falling. Results showed a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. As there are many different approaches to fall detection [17], the current paper focuses on the detection of forward falls with an emphasis on the type of fall, mainly if it was a sudden undamped fall or a damped fall. The current paper presents a method for classifying fall events, such as forward falls and forward damped falls, from normal day-to-day activities by combining normal gait acceleration data from real persons and fall acceleration data from digital models as inputs to train supervised learning models. To develop an accurate method to establish if a fall has occurred or normal activities have been carried out by the monitored person, in the current paper, the inertial data acquired using an accelerometer on the X and Y axes, mounted in the pelvis area, will be used for both digital and real-world measurements. Due to the high risk of injury as well as the long period of time required for volunteers to simulate real falls, as well as the large number of volunteers required to generate the necessary input data to train supervised learning models to detect falls, this paper validates the possibility of using digital models to generate training data. The digital model is generated starting from a four-link walker model in the passive dynamic simulation example from OpenSim [18], which is further developed to simulate a real person. The digital model has specific contact joints and forces, to be able to simulate real-world movements [19; 20]. The model is generated using body segments and inertial data from the literature [21]. Head and torso geometries are defined as spheres, and the upper arm, forearm, thigh, and calf are defined as ellipses with their inertial properties given using the inertial properties presented by [PERSON] et al. in report [21], which is a thorough investigation that looks at the physical characteristics connected to the dynamics of the human body with an emphasis on the inertial properties. The data provided in article [21] is important because it clarifies how the human body reacts to motion and force, which is important in domains like biomechanics, ergonomics, automobile safety, and robotics. The study looks at the distribution of mass in the human body and how that influences the responses of various body components to motions and forces from the outside world. The report details the methods used to collect data, including experimental setups, measurement techniques, and analytical models for defining the inertial parameters of several subjects. The authors of this study analyzed the inertial properties of six different corpses. The digital model developed in the current paper considers the arithmetic mean of the anthropometric parameters of the six measured subjects and is considered symmetrical, and according to paper [21], the inertial parameters of the main body segments are shown in Table 1.  
After the model is generated, the forward dynamics tool is used to record the model's acceleration on the X and Y axis.