# Forward Fall Detection Using Inertial Data and Machine Learning
## 6 Conclusions  
Fall risk assessment is a very important concern for providing immediate needed assistance before it can pose a health risk. The detection of falls and their type as promptly as possible can contribute significantly to the shortening of the recuperation time and overall health of a person. Special devices can be developed that can deploy and protect the vital parts when the fall event has been detected and to inform specialized personnel that the person needs immediate assistance.  
In this work, emphasis is placed on generating training data by introducing simulation procedures with the help of specialized programs as well as anthropometric data obtained from literature. The paper highlights the advantages and availability of data that can be generated using digital models.  
By using the OpenSim software, a model of a person consisting of 11 segments, a height of 170.52 cm and a weight of 62.25 kg was generated. The digital model can simulate front falls both with damping and without, making it simpler to replicate real fall events of different people.  
Using the data generated through simulation, LSTM and Fine KNN models were trained to detect falls with very good accuracy, based on the raw acceleration data. In the training process, the models were evaluated using 20% of the generated data for testing, with the results illustrating a very good performance.  
For the experimental measurements, the LSTM model detected the fall events with very high accuracy, but did not successfully differentiate the type of fall event.  
Both developed models detected the fall event with good accuracy, which brings us to the conclusion that the fall detection model can take advantage of both methods by preprocessing the output of the LSTM and feeding it into a KNN classifier, which is competent at handling high-dimensional data and identifying local patterns. By further employing exhaustive research considering different positions of the sensors and several types of falls, the presented methods can make use of portable technologies like mobile phones or smart watches to be able to predict fall events in real time, thus, becoming a reliable fall detection system.  
The presented results showed a high accuracy despite both the generated and the measured data being taken directly into the network without prior filtering and processing.  
**Author Contributions:** Conceptualization, C.T. and G.-R.G.; methodology, C.T. and Z.-I.P.; software, C.T.; validation, C.T. and G.-R.G.; formal analysis, G.-R.G. and A.I.B.; investigation, C.T.; resources, C.T. and T.-L.H.; data curation, C.T.; writing--original draft preparation, C.T.; writing--review and editing, G.-R.G.; visualization, A.I.B.; supervision, G.-R.G.; project administration, C.T.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by University Babes-Bolyai, grant number SRG-UBB 32911/22.06.2023.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Research Ethics Subcommittee of the Babes-Bolyai University of Cluj-Napoca, according to the Research Ethics Approval no. 15.796/16 July2024.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.