A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.
A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.
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DOI：
10.1088/0967-3334/35/11/2191
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发表时间：
2014-11
影响因子：
3.2
通讯作者：
Marshall S
中科院分区：
文献类型：
--
作者：
Ellis K;Kerr J;Godbole S;Lanckriet G;Wing D;Marshall S
腕部加速度计被用于体力活动（PA）的人群水平监测，但需要更多的研究来评估其正确分类PA行为类型和预测能量消耗（EE）的有效性。在这项研究中，我们比较了佩戴在手腕和臀部的加速度计，以及心率（HR）数据的附加值，用于使用机器学习预测PA类型和EE。40名成年人在实验室环境中进行运动和家务活动，同时佩戴三个ActiGraph GT3X+加速度计（左髋、右髋、非优势手腕）和心率监测仪（Polar RS400）。参与者还佩戴便携式间接热量计（COSMED K4b2），计算每分钟的EE和代谢当量（MET）。我们开发了两个预测模型：一个随机森林分类器来预测活动类型，一个随机森林回归树来估计MET。预测进行了评价，使用留一用户交叉验证。髋关节加速度计在预测四种活动类型（家庭，楼梯，步行，跑步）方面的平均准确度为92.3%，而腕关节加速度计的平均准确度为87.5%。在所有8项活动（洗衣、擦窗、除尘、洗碗、扫地、爬楼梯、走路、跑步）中，髋关节和腕关节加速度计的平均准确度分别为70.2%和80.2%。单独使用髋关节或腕关节设备预测MET分别获得每6分钟回合1.09和1.00 MET的均方根误差（rMSE）。包括HR数据改善了MET估计，但没有显著改善活动类型分类。这些结果证明了随机森林分类和回归森林的有效性PA类型和MET预测使用加速度计。腕部加速度计被证明在预测具有显著手臂运动的活动方面更有用，而髋部加速度计在预测运动和估计EE方面更上级。
Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6-minute bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.
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DOI：
10.1097/00005768-199805000-00021
发表时间：
1998-05-01
期刊：
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
影响因子：
--
作者：
Freedson, PS;Melanson, E;Sirard, J
通讯作者：
Sirard, J
影响因子：
22.7
作者：
Shotton, Jamie;Sharp, Toby;Moore, Richard
通讯作者：
Moore, Richard
影响因子：
5.2
作者：
Ellis K;Godbole S;Marshall S;Lanckriet G;Staudenmayer J;Kerr J
通讯作者：
Kerr J
影响因子：
3.3
作者：
Brage, S;Brage, N;Wareham, NJ
通讯作者：
Wareham, NJ
影响因子：
3.3
作者：
Staudenmayer, John;Pober, David;Freedson, Patty
通讯作者：
Freedson, Patty