# Forward Fall Detection Using Inertial Data and Machine Learning
## 5 Discussion  
In this paper, a digital model was developed and used to simulate forward damped and undamped falls to create a database composed of simulated normalized acceleration values. The generated data consisting of simulated falls and experimental gait acceleration values were used to train LSTM and Fine KNN models with the main scope of detecting fall events and even classifying the type of fall. For testing the models in real-world scenarios, six male volunteers with diverse physical characteristics (with ages ranging from 38 to 52, weights from 68 to 94 kg, and heights from 173 to 184 cm) participated in the experiments. The sensor placement (front or back of the pelvis) and the type of fall simulation (damped vs. undamped) provided the dataset for testing the models. The LSTM network demonstrated very good accuracy in predicting the fall event from normal activity. The classification results indicate that forward falls were misclassified as damped falls for subjects 1 to 4. This suggests that while the network is effective at reliably detecting fall events overall, it struggles to accurately differentiate between specific types of falls. Therefore, the model performs well in identifying that a fall has occurred, but its accuracy in distinguishing between fall types (such as forward vs. damped) is less reliable. For normal walking activities, the LSTM network also demonstrated high accuracy, with predictions closely matching the expected target, obtaining a mean predicted value of 0, showcasing that the model can successfully distinguish between normal activities from fall events. In distinguishing falls from normal activities like sitting in bed, the LSTM network maintained a low maximum output, having a mean predicted value of 0.04, which would allow for a threshold to be set that prevents false positives for non-fall activities. The Fine KNN model was tested using labeled experimental data for various scenarios, including damped falls, forward undamped falls, normal gait, and sitting in bed. From the presented results, the KNN classifier was observed to predict a consistent misclassification issue where damped falls were classified as forward falls, but not predominantly. This indicates the model's sensitivity to damping actions, possibly due to subjects' natural tendency to dampen their falls either by using their hands or knees even when instructed otherwise. Confusion matrices for each subject illustrated the model's prediction accuracy. The results showed that the KNN model could accurately classify falls from normal activities, with good accuracy. Fall detection models combining the LSTM neural network and KNN classifier will produce predictions that are more accurate, even when they are trained with digital models. Acceleration profiles during falls and regular activities are two examples of sequential data that the LSTM is excellent at capturing temporal relationships and patterns in. An innovative advantage of the presented method is the use of digital simulation to generate training data. This approach is easy to implement, carries no risk to subjects, and, to our knowledge, has seen limited use in existing research. The presented method's accuracy and practicality make it a promising candidate for real-world applications, particularly in wearable devices. The classification between damped and undamped falls is of high importance in determining if the person has suffered a potentially fatal fall or not. In reference [9], the LSTM model trained on synthetic IMU data from the shank achieves an accuracy of 91.99% on Dataset A, which is significantly higher than the model trained on real data (87.5%). This synthetic data-based model also outperforms the real data-trained model across other performance metrics. In contrast, the current paper's results demonstrate that, aside from the instances with 92.5% and 93.4% accuracy, the model performs exceptionally well, often achieving accuracy rates above 95%.