Predicting lying, sitting, walking and running using Apple Watch and Fitbit data.
Predicting lying, sitting, walking and running using Apple Watch and Fitbit data.
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DOI：
10.1136/bmjsem-2020-001004
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发表时间：
2021
影响因子：
4.8
通讯作者：
A Basset F
中科院分区：
文献类型：
--
作者：
Fuller D;Anaraki JR;Simango B;Rayner M;Dorani F;Bozorgi A;Luan H;A Basset F
这项研究的目的是检查商业可穿戴设备是否可以准确地预测躺下，坐着以及不同强度的步行和跑步。我们招募了49名参与者（23名男性和26名女性）佩戴三种设备，Apple Watch Series 2，Fitbit Charge HR 2和iPhone 6S。参与者完成了一个65分钟的协议，包括40分钟的总跑步机时间和25分钟的坐或躺的时间。该研究的结果变量是六种运动类型：躺、坐、自行步行和步行/跑步，其代谢当量为3个任务（MET）、5个MET和7个MET。所有分析都是在分钟级别进行的，包括心率，步数，距离和卡路里，来自Apple Watch和Fitbit。其中包括三种不同的机器学习模型：支持向量机，随机森林和旋转森林。我们的数据集分别包括3656分钟和2608分钟的Apple Watch和Fitbit数据。旋转森林模型对Apple Watch的分类准确率最高，为82.6%，随机森林模型对Fitbit的分类准确率最高，为90.8%。Apple Watch数据的分类准确率从坐姿的72.6%到7个MET的89.0%不等。对于Fitbit，准确率从坐着的86.2%到7个MET的92.6%不等。这项初步研究表明，来自商用可穿戴设备的数据可以合理准确地预测运动类型。需要更多的研究，但这些方法是使用商业可穿戴设备数据在人群水平上进行运动类型分类的概念证明。
This study’s objective was to examine whether commercial wearable devices could accurately predict lying, sitting and varying intensities of walking and running. We recruited a convenience sample of 49 participants (23 men and 26 women) to wear three devices, an Apple Watch Series 2, a Fitbit Charge HR2 and iPhone 6S. Participants completed a 65 min protocol consisting of 40 min of total treadmill time and 25 min of sitting or lying time. The study’s outcome variables were six movement types: lying, sitting, walking self-paced and walking/running at 3 metabolic equivalents of task (METs), 5 METs and 7 METs. All analyses were conducted at the minute level with heart rate, steps, distance and calories from Apple Watch and Fitbit. These included three different machine learning models: support vector machines, Random Forest and Rotation forest. Our dataset included 3656 and 2608 min of Apple Watch and Fitbit data, respectively. Rotation Forest models had the highest classification accuracies for Apple Watch at 82.6%, and Random Forest models had the highest accuracy for Fitbit at 90.8%. Classification accuracies for Apple Watch data ranged from 72.6% for sitting to 89.0% for 7 METs. For Fitbit, accuracies varied between 86.2% for sitting to 92.6% for 7 METs. This preliminary study demonstrated that data from commercial wearable devices could predict movement types with reasonable accuracy. More research is needed, but these methods are a proof of concept for movement type classification at the population level using commercial wearable device data.
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影响因子：
5
作者：
Dooley EE;Golaszewski NM;Bartholomew JB
通讯作者：
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影响因子：
2.2
作者：
Freedson, PS;Miller, K
通讯作者：
Miller, K
DOI：
10.1249/mss.0000000000000597
发表时间：
2015-08-01
期刊：
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
影响因子：
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作者：
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通讯作者：
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影响因子：
2.9
作者：
Bunney PE;Zink AN;Holm AA;Billington CJ;Kotz CM
通讯作者：
Kotz CM
DOI：
10.1109/tpami.2006.211
发表时间：
2006-10-01
影响因子：
23.6
作者：
Rodriguez, Juan J.;Kuncheva, Ludmila I.
通讯作者：
Kuncheva, Ludmila I.