D-TWO-H: Dynamic Time Warping for the Optimisation of Hypertension

“众所周知，通过可穿戴传感器进行的动态血压（BP）监测有可能在许多新环境中实现与健康相关的警戒和医疗护理的新水平，例如，控制慢性高血压和监测康复期间的住院患者。然而，实现真正的非侵入性血压（NIBP）测量的一个重大挑战仍然是考虑潜在动脉壁未知张力的问题：如果简单地测量动脉外的压力（例如，在覆盖的皮肤上），则测量动脉内压力和快速变化的动脉壁张力的平衡。理想的NIBP方法解决了独立于动脉壁张力估计动脉壁内压力的问题。然而，对于真正可穿戴的NIBP测量，还没有最佳的解决方案。理想的可穿戴设备应该是重量轻、易于使用、非侵入性、体积小、不显眼，并且像普通的手表一样几乎难以察觉。机器学习的基本假设是，可以通过研究过去的数据模型来构建分析解决方案。机器学习支持从以前的数据模型、趋势、模式中学习的数据分析，并基于该研究构建自动化的算法系统。由于机器学习完全依赖于预先构建的算法来进行数据驱动的分析和预测，因此它声称将取代人类执行的数据分析和预测任务。在机器学习中，算法具有从过去的数据中学习和学习的能力，然后通过预测分析和决策树模拟人类的决策过程。动态时间翘曲是一种时间算子机器学习算法架构，专门用于寻找具有一定限制的两个给定序列（例如时间序列）之间的最佳匹配。这些序列在时间维度上被非线性地“扭曲”，以确定一个独立于时间维度上某些非线性变化的相似性度量。这种序列比对方法常用于时间序列分类。虽然DTW测量两个给定序列之间的类似距离的量，但它不能保证三角不等式成立。D-TWO-H希望使用一种独特配置的机器学习算法来识别光学传感器样本之间的趋势，从而开发动脉性能地图，从而允许用户计算血压趋势值。希望这些工作将使连续血压的分辨率成为消费者健康可穿戴设备可以获得的指标。”

"It is well recognized that ambulatory blood pressure (BP) monitoring by means of wearable sensors has the potential to enable new levels of health-related vigilance and medical care in a number of novel settings, including, for example, controlling chronic hypertension and monitoring in-patients during convalescence.However, a significant challenge to realizing true non-invasive blood pressure (NIBP) measurement remains the problem of accounting for the unknown tension in the underlying arterial wall: If one simply measures pressure external to an artery (for instance, on the overlying skin), one is measuring the balance of intra-arterial pressure and the rapidly varying arterial wall tension.Ideal NIBP methods solve the problem of estimating intra-arterial wall pressures independently of the arterial wall tension. Yet, there is no optimal solution to truly wearable NIBP measurement. The ideal wearable device would be lightweight, easy-to-apply, non-invasive, small, unobtrusive, and as close to imperceptible as a regular wrist-watch.The fundamental assumption in Machine Learning is that analytical solutions can be built by studying past data models. Machine Learning supports that kind of data analysis that learns from previous data models, trends, patterns, and builds automated, algorithmic systems based on that study.As Machine Learning relies solely on pre-built algorithms for making data-driven analysis and predictions, it claims to replace data analytics and prediction tasks carried out by humans. In Machine Learning, the algorithms have the capability to study and learn from past data, and then simulate the human decision-making process by using predictive analysis and decision trees.Dynamic Time Warping is a temporal operator Machine Learning Algorithm architecture that specialises in finding the optimal match between two given sequences (e.g. time series) with certain restrictions. The sequences are ""warped"" non-linearly in the time dimension to determine a measure of their similarity independent of certain non-linear variations in the time dimension. This sequence alignment method is often used in time series classification. Although DTW measures a distance-like quantity between two given sequences, it doesn't guarantee the triangle inequality to hold.D-TWO-H looks to use a uniquely configured Machine Learning Algorithm to identify trends between optical sensor samples and thus develop a map of arterial performance which can thus allow a user to calculate a value for trending Blood Pressure. It is hoped that these works will enable the resolution of a continuous Blood Pressure as a metric that can be acquired by consumer health wearable devices."