Abstract
Background A wearable sensor (WS) is a prominent technology application that senses and gathers information from a user for analyzing changes in physiological signs. Analyzing the physiological sign differences enables the better healthcare solutions. Purpose This paper introduces an unsynchronized sensor data analytics (USDA) model for the effective handling of wearable device data regardless of the time factor. Time-dependent healthcare treatments and diagnosis are the themes on which this analytics model focuses. Methods The gathered WS data is classified depending on the time factor and data frequency of occurrence. This occurrence frequency is correlatively analyzed using the diagnosis module to identify defects and to fulfill the missing sensor data consideration. Healthcare diagnoses requiring immediate responses and timely solutions for patients/end-users rely on this model for uncompromising analysis. Results The vital changes in WS data and time factors are analyzed using sophisticated machine learning methods for previous diagnosis correlation and effective accuracy. Conclusion Responsive healthcare solutions using unsynchronized WS data help to achieve better efficiency and reduce complications in assessing the performance of the healthcare systems.