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Convergence of smartphone technology and algorithms that estimate physical activity for cardiac rehabilitation

机译:智能手机技术和算法的融合,这些技术和算法可估计心脏康复的身体活动

摘要

Completing a cardiac rehabilitation program (CRP) following myocardial infarction has many health benefits, but CRP is not completed by all patients. This thesis investigated if completion rates could be improved by providing patients with a smartphone and telehealth equipment whilst enrolled in the CRP, and if there was a relationship between the amount of physical activity they completed during their enrolment, and the six minute walking distance.In order to estimate the amount of physical activity completed by participants whilst enrolled in a CRP, a model capable of recognising five human activities (postural transitions, stationary periods, walking on level ground as well as up and down stairs) by analysing the measurements from the smartphone’s internal sensors was developed using sensor data collected from both younger and older adults. This model (with an average total classification accuracy of 90.4% and average Cohen’s kappa of 0.83) was incorporated into an application which was installed on the smartphone of participants who were in the intervention arm of the study. Additional methods were also investigated that might enable the model to more accurately differentiate between standing, and sedentary periods in future. A computationally lightweight method – complementary attitude and heading reference system, was developed that estimated the attitude of a magnetic and inertial measurement unit (root mean square error in the pitch, roll and yaw angles of 1.84 degrees, 3.37 degrees, and 4.83 degrees, respectively). Incorporating this method into a new model for recognising human activities improved the model’s performance due to its use of an attitude invariant feature that calculated the angle between the average attitude during upright periods and the average attitude over the previous 2.5 seconds. This feature enabled the standing (class sensitivity 80%) and sedentary (class sensitivity 97%) classes to be separated, regardless of the smartphone’s attitude in the pants pocket.The results of a randomised controlled trial in which participants were recruited from a hospital-based CRP to receive the proposed adjunct or complete the standard CRP identified a significant difference in completion rates between treatment groups (88% vs 67%; p = 0.038) in favour of those randomised to the intervention group. This suggests that the telehealth adjunct increased the likelihood that participants would complete the program.
机译:在心肌梗死后完成心脏康复计划(CRP)具有许多健康益处,但并非所有患者都完成CRP。本论文研究了在参加CRP时向患者提供智能手机和远程医疗设备是否可以提高完成率,并且在入学期间完成的体育锻炼量与六分钟的步行距离之间是否存在关系。为了估算参加CRP时参与者完成的体育活动的数量,该模型能够通过分析来自CRP的测量值来识别五种人类活动(姿势转变,固定时期,在水平地面上以及上下楼梯)智能手机的内部传感器是使用从年轻人和老年人那里收集的传感器数据开发的。该模型(平均总分类准确度为90.4%,平均科恩kappa为0.83)已合并到一个应用程序中,该应用程序安装在参与研究的参与者的智能手机上。还研究了其他方法,这些方法可以使模型更准确地区分将来的站立时间和久坐时间。开发了一种计算轻量级的方法–互补的姿态和航向参考系统,用于估算磁和惯性测量单位的姿态(俯仰角,横滚角和偏航角的均方根误差分别为1.84度,3.37度和4.83度) )。将这种方法结合到用于识别人类活动的新模型中,由于使用了姿态不变功能,该功能可以计算出直立期间的平均姿态与之前2.5秒内的平均姿态之间的夹角,从而提高了模型的性能。这项功能可以将站立(班级敏感度为80%)和久坐(班级敏感度为97%)的类别分开,而无论智能手机在裤兜中的姿势如何。随机对照试验的结果是从医院招募参与者,以接受建议的辅助药物或完成标准CRP的CRP为基础,发现治疗组之间的完成率有显着差异(88%比67%; p = 0.038),而随机分配给干预组的人则更是如此。这表明远程医疗辅助设备增加了参与者完成该程序的可能性。

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