Design Of Smart Shoe System To Avoid Human Fall

Abstract

To enhance the quality of life for elderly and physically challenged individuals and reduce the risk of falls, this research proposes a novel machine learning-based approach. Our system prototype includes a smartphone and a smart shoe with four pressure sensors and a Wi-Fi communication module. Fall detection is achieved through a decision tree algorithm, which segregates normal and cautious gait values and issues alerts via messages, emails, or calls when cautious gait is detected. This innovative system has the potential to significantly decrease the likelihood of falls among elderly individuals, enabling them to live more independently while safeguarding their well-being. Because of the quick response, this system provides more peaceful to working individuals for their parents.