A Novel Method for Human Fall Detection Using Federated Learning and Interval-Valued Fuzzy Inference Systems

Abstract

Abstract This study introduces an innovative interval-valued fuzzy inference system (IFIS) integrated with federated learning (FL) to enhance posture detection, with a particular emphasis on fall detection for the elderly. Our methodology significantly advances the accuracy of fall detection systems by addressing key challenges in existing technologies, such as false alarms and data privacy concerns. Through the implementation of FL, our model evolves collaboratively over time while maintaining the confidentiality of individual data, thereby safeguarding user privacy. The application of interval-valued fuzzy sets to manage uncertainty effectively captures the subtle variations in human behavior, leading to a reduction in false positives and an overall increase in system reliability. Furthermore, the rule-based system is thoroughly explained, highlighting its correlation with system performance and the management of data uncertainty, which is crucial in many medical contexts. This research offers a scalable, more accurate, and privacy-preserving solution that holds significant potential for widespread adoption in healthcare and assisted living settings. The impact of our system is substantial, promising to reduce the incidence of fall-related injuries among the elderly, thereby enhancing the standard of care and quality of life. Additionally, our findings pave the way for future advancements in the application of federated learning and fuzzy inference in various fields where privacy and precision in uncertain environments are of paramount importance.