###### Abstract  
Hospitals must continually monitor their patients' actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as \"categories\". Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject's mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4 SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate. 4 SFNN; PSO; AEC; prediction; accuracy; MAE; falling; MSE; SDL; FOL; FKL +
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soon as the data reaches the targeted servers, it is mined using machine learning and deep learning-inspired algorithms. This study aims to establish techniques for classifying human activities, with the ultimate objective of recognizing and preventing mobility issues due to its results. Movement abnormality exercises are crucial for avoiding deadly crashes or slips because older adults are more likely to have unexpected mobility limits due to health conditions. Human behavior is difficult to observe, since it is complex and occasionally surprising. As a result, it is difficult to establish a stable connection that can be used to influence the conduct of others. Due to the tremendous number and diversity of data available for human tracking and monitoring, predicting how people will perceive and respond to any given set of circumstances is exceedingly challenging. The quest to uncover data-driven alternatives to present employment activities drives advancement in this sector's artificial intelligence (AI). This article defines the category as an intelligent monitoring system that uses AI-powered apps to monitor the movement of elderly or senior citizens, pregnant women, severely injured patients, and so on. Previous research has shown that using these strategies to treat several conditions is effective. Wearable sensor systems, such as smartwatches, smartphones, and other mobile computing devices, are now being used in several settings, including hospitals, smart homes, libraries, and other public and private areas. Interference with communication, such as shadowing, noise, interactions with other communication systems, and so on, as well as noise inherited from human organs, are some examples of elements that may delay information transfer (e.g., when heartbeats are monitored, muscular motions or other organs can be interfaced with the electrocardiogram signal). As a result, it is possible that systems that recognize actions or behaviors will not function properly [4]. The second difficulty is the presence of data that has not been labeled. These are the kind of sources that students typically use to gain access to the sensitive subject matter discussed in class. All data collectors simply acquire the information they want without first classifying it, which raises processing costs [5]. Wearing smart devices designed to avoid falls presents a distinct set of challenges.