Objectives This study’s objective was to examine whether commercial wearable devices could accurately predict lying, sitting and varying intensities of walking and running. Methods 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. Results 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. Conclusion 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. Data are available in a public, open access repository. Data are available at this link: .

中文翻译：

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使用Apple Watch和Fitbit数据预测躺着，坐着，走路和跑步

目的这项研究的目的是检验商用可穿戴设备是否可以准确预测躺卧，坐着以及步行和跑步强度的变化。方法我们收集了49名参与者（23名男性和26名女性）的便利性样本来穿戴三种设备，分别是Apple Watch Series 2，Fitbit Charge HR2和iPhone 6S。参与者完成了65分钟的实验方案，包括40分钟的跑步机总时间和25分钟的坐着或躺着时间。该研究的结果变量为六种运动类型：躺着，坐着，自定步伐行走和以3个代谢当量任务（MET），5个MET和7个MET行走/跑步。所有分析都是在分钟级别进行的，其中包括Apple Watch和Fitbit的心率，步距，距离和卡路里。其中包括三种不同的机器学习模型：支持向量机，随机森林和自转森林。结果我们的数据集分别包含3656分钟和2608分钟的Apple Watch和Fitbit数据。Rotation Forest模型对Apple Watch的分类准确度最高，为82.6％，而Random Forest模型对Fitbit的准确性最高，为90.8％。Apple Watch数据的分类准确性范围从坐姿的72.6％到7 MET的89.0％不等。对于Fitbit，坐姿的准确度在86.2％之间，对于7 METs的准确度在92.6％之间。结论这项初步研究表明，来自商用可穿戴设备的数据可以合理地预测运动类型。需要进行更多的研究，但是这些方法是使用商业可穿戴设备数据在人群级别进行运动类型分类的概念验证。数据可在公共的开放访问存储库中获得。。