# Forward Fall Detection Using Inertial Data and Machine Learning  
[PERSON]  
1 Department of Engineering Science, Babes-Bolyai University, Str. [PERSON] 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (C.T. ); [EMAIL_ADDRESS] (G.-R.G.) [PERSON]  
1 Department of Engineering Science, Babes-Bolyai University, Str. [PERSON] 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (C.T. ); [EMAIL_ADDRESS] (G.-R.G.) [PERSON]  
1 Department of Engineering Science, Babes-Bolyai University, Str. [PERSON] 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (C.T. ); [EMAIL_ADDRESS] (G.-R.G.) [PERSON]  
2 Doctoral School of Engineering, Babes-Bolyai University, Str. [PERSON] 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (C.T.) [PERSON]  
2 Doctoral School of Engineering, Babes-Bolyai University, Str. [PERSON] 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (C.T.) Footnote 2: email: td.gov.t. noncontact-based sensors and others using deep learning and machine-learning techniques [3]. In paper [3], the authors used two methods for detecting falls, one which used a video camera and one which used accelerometer sensors that are commonly used in smartphones. For the contact-based method, 1D Convolutional Neural Network (CNN) and machine learning (ML) methods were used, and for the recordings, 3D ConvNets using three different architectures were used: BGG-16, Xception, and DenseNet, which obtained an accuracy of 99.85% using the UP-Fall detection dataset [4] for training and testing. However, this study had drawbacks in that the methods were not tested against new experimental data. Fall detection using inertial sensors has emerged as a practical tool for ongoing fall risk assessment in different environments. In article [5], the authors showcased the use of inertial data coupled with deep learning models, specifically Long Short-Term Memory (LSTM) neural networks by considering the spatio-temporal gait parameters as input, obtaining a good classification accuracy. In paper [6], the authors conducted a comprehensive study on developing a fall detection system using artificial intelligence which was tailored for telemedicine applications, specifically targeting elderly care, using a Kinect 2.0 camera, manufactured by Microsoft, Redmond, WA, USA, and an AI model that analyzes images in real-time. The initial approach used libraries such as Bounding Boxes to determine falls based on the lengths of the sides. However, this approach faced challenges due to the inclusion of extraneous reference points in the generated point cloud, which led to inaccurate classifications. Additionally, the current model relies on a laptop for processing and the Kinect camera, which presents some limitations in terms of portability and usability in real-world applications. Another important aspect is that falls can occur in different ways [7], which can be forward, sideward, backward, damped, and undamped. From the existing studies [7; 8; 9; 10; 11], it was concluded that forward falls are the most prominent. In paper [12], the author describes the detection of falls with a 3-axis digital accelerometer by successfully classifying acceleration change characteristics while falling from different activities like walking downstairs, upstairs, sitting down, and standing up. To develop an effective classification or prediction model for falls and faints, it is necessary to have a sizeable participant database. However, collecting data from many participants becomes time-consuming, error-prone, and expensive due to the requirement for continuous data collection and long-term follow-up. In paper [13], the authors present a method of acquiring training data using a forward kinematic approach with unmarked 3D motion capture technology and the biomechanical simulation platform Open Sim [14]. After training an LSTM model, accuracies of 91.99% and 86.62% were obtained on two different datasets of real fall-related inertial measuring unit (IMU) data.