Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions.
Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions.
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DOI：
10.1016/j.compbiomed.2021.104633
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发表时间：
2021-08
影响因子：
7.7
通讯作者：
Dassau E
中科院分区：
文献类型：
--
作者：
Cescon M;Choudhary D;Pinsker JE;Dadlani V;Church MM;Kudva YC;Doyle Iii FJ;Dassau E
本文介绍的方法来估计身体活动和久坐不动的行为方面的三轴加速度计的数据收集与腕戴式设备在32 [Hz]的采样率对成人1型糖尿病（T1D）在自由生活条件。特别是，我们提出了两种方法，能够检测和分级活动的强度和个人健身久坐，轻度，中度或剧烈的基础上，和一种方法，在监督学习框架中进行活动分类，以预测特定的用户行为。活动水平分级的人群结果显示，多类平均准确率为99.99%，精确度为98.0± 2.2%，召回率为97.9±3.5%，F1评分为0.9±0.0。对于特定行为预测，我们的性能最好的分类器，给出了群体多类平均准确率为92.43± 10.32%，精确率为92.94± 9.80%，召回率为92.20±10.16%，F1得分为92.56± 9.94%。我们的调查表明，可以从T1D患者在自由生活条件下收集的三轴加速度计数据中检测，分级和分类身体活动和久坐行为，具有良好的准确性和精度。这在糖尿病自动血糖控制系统的背景下尤其重要，因为我们提出的方法有可能根据身体活动的强度来通知治疗参数的变化，从而使患者能够满足其血糖目标。
This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0±2.2%, recall of 97.9±3.5% and F1 score of 0.9±0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43±10.32%, precision of 92.94±9.80%, recall of 92.20±10.16% and F1 score of 92.56±9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.
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影响因子：
2.5
作者：
Pinsker, Jordan E.;Kraus, Amy;Kerr, David
通讯作者：
Kerr, David
影响因子：
5.4
作者：
Garg SK;Weinzimer SA;Tamborlane WV;Buckingham BA;Bode BW;Bailey TS;Brazg RL;Ilany J;Slover RH;Anderson SM;Bergenstal RM;Grosman B;Roy A;Cordero TL;Shin J;Lee SW;Kaufman FR
通讯作者：
Kaufman FR
影响因子：
16.2
作者：
Brazeau, Anne-Sophie;Rabasa-Lhoret, Remi;Strychar, Irene;Mircescu, Hortensia
通讯作者：
Mircescu, Hortensia
影响因子：
5
作者：
Dasanayake, Isuru S;Bevier, Wendy C;Dassau, Eyal
通讯作者：
Dassau, Eyal
DOI：
10.1109/titb.2007.899496
发表时间：
2008-01-01
影响因子：
--
作者：
Ermes, Miikka;Parkka, Juha;Korhonen, Ilkka
通讯作者：
Korhonen, Ilkka