Study of Various Machine Learning Algorithms for Elderly Fall Prediction and Detection

Falls substantially occur among senior and physically challenged people which results in severe injuries and indeed beget deaths. In order to cover a person from injuries without counting on others, study suggests a machine literacy- grounded fall forestallment and discovery system, which will ameliorate their quality of life. The need of mitigating fall incidents among the elderly populace necessitates the emergence of predictive and detection mechanisms. This study explores an array of machine learning algorithms, encompassing but not limited to Decision Trees, Support Vector Machines, Neural Networks, and Ensemble Methods, to discern their efficacy in forecasting and identifying fall occurrences. The investigation employs an extensive dataset, curated to encapsulate multifaceted parameters pertinent to fall incidents, including but not confined to gait analysis, physiological signals, and environmental conditions. Rigorous pre-processing techniques, coupled with advanced feature extraction methodologies, are deployed to augment the predictive efficacy of the algorithms. In order to create a multifactorial fall prediction system, feature extraction techniques based on fall instrumentation were studied. In order to improve automatic fall detection systems, we took into account the influence of multiple factors on the system's performance: the kind of dataset, which consists of simulated or real-world data; the on-body positions where the wearable device is coupled; and limitations associated with the deployment hardware, like sampling rate, sensitivity level of the algorithm, and complexity of the model.