  Falls among individuals, especially the elderly population, can lead to
serious injuries and complications. Detecting impact moments within a fall
event is crucial for providing timely assistance and minimizing the negative
consequences. In this work, we aim to address this challenge by applying
thorough preprocessing techniques to the multisensor dataset, the goal is to
eliminate noise and improve data quality. Furthermore, we employ a feature
selection process to identify the most relevant features derived from the
multisensor UP-FALL dataset, which in turn will enhance the performance and
efficiency of machine learning models. We then evaluate the efficiency of
various machine learning models in detecting the impact moment using the
resulting data information from multiple sensors. Through extensive
experimentation, we assess the accuracy of our approach using various
evaluation metrics. Our results achieve high accuracy rates in impact
detection, showcasing the power of leveraging multisensor data for fall
detection tasks. This highlights the potential of our approach to enhance fall
detection systems and improve the overall safety and well-being of individuals
at risk of falls.

Signal Processing;Computer Vision and Pattern Recognition;Machine Learning