Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning

Wearable Sensor (WS)-based monitoring systems detect minute patient movements/ demands and abnormalities through periodic sensing and imaging. Sensor data observed over different intervals is not constant or available based on operating sequences. Due to variations in data sequences, the analysis process becomes complex, resulting in less precise outputs. To address this problem, an Allied Data Disparity Technique (ADDT) is proposed in this article. This technique identifies the disparity in different monitoring sequences in coherence with the clinical and previous values. Based on the mean disparity, the data requirement for the WS sequence is decided. This decision uses multiple substituted and predicted values obtained from previous instances. Multi-Instance Ensemble Perceptron Learning is used in this decision process, where the substitution instances for clinical and previous outcomes are performed. The ensemble perceptron selects the maximum clinical value correlating sensor data to ensure high sequence prediction. The ensembles are updated based on the highest precision-based WS values for diagnosis. This diagnosis-focused coalition between clinical and predicted WS values is updated periodically for the new precision levels identified.