A fair dividend approach for aggregating wearable sensor data to improve electronic health records

Abstract

Wearable sensor (WS) technology in healthcare is essential because it makes medical diagnosis easier by continuously monitoring important changes in an individual’s body. This technology is used to detect aberrant occurrences and predict medical dangers. A central connecting unit is used to stream and send accurate observations to improve the quality of medical diagnosis. In this paper, we present a Fair Dividend Interrupt Method (FDIM), a new way to arrange and improve the efficiency of combining WS inputs. This approach employs federated learning to prioritize interruptions based on their importance and WS criteria. This leads to well-structured streaming periods across numerous connecting devices, guaranteeing continuous sequences. The sequence determination uses balanced linear scheduling, optimizing the structure of sensing operations and increasing WS input availability when interruptions from multiple sensors, thereby boosting operating efficiency. The proposed approach outperforms baseline methods in access time, computational complexity, data utilization, processing time, aggregation ratio, and error rate by 10.18%, 5.19%, 10.57%, 8.48%, and 10.42%, respectively. Due to these developments, FDIM is now a highly efficient, scalable solution for wearable healthcare systems that allows accurate medical decision-making.