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.

中文翻译：

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不同步的可穿戴传感器数据分析模型，可提高智能医疗系统的性能

背景

可穿戴式传感器（WS）是一项杰出的技术应用程序，可感测并收集来自用户的信息以分析生理征兆的变化。分析生理体征差异可以提供更好的医疗保健解决方案。

目的

本文介绍了一种不同步的传感器数据分析（USDA）模型，可有效处理可穿戴设备数据，而不受时间因素的影响。基于时间的医疗保健和诊断是此分析模型关注的主题。

方法

根据时间因素和出现的数据频率对收集的WS数据进行分类。使用诊断模块对发生频率进行相关分析，以识别缺陷并满足缺失的传感器数据考虑。对于患者/最终用户而言，需要即时响应和及时解决方案的医疗保健诊断依靠此模型进行毫不妥协的分析。

结果

使用先进的机器学习方法分析WS数据和时间因素的重要变化，以实现先前的诊断关联和有效的准确性。

结论

使用非同步WS数据的响应式医疗保健解决方案有助于提高效率，并减少评估医疗保健系统性能时的复杂性。