CARL: a running recognition algorithm for free-living accelerometer data
CARL: a running recognition algorithm for free-living accelerometer data
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DOI：
10.1088/1361-6579/ac41b8
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发表时间：
2021-11-01
影响因子：
3.2
通讯作者：
Gruber, Allison H.
中科院分区：
文献类型：
--
作者：
Davis, John J.;Straczkiewicz, Marcin;Gruber, Allison H.
可穿戴式加速度计在身体活动流行病学和运动生物力学方面有着巨大的应用前景。然而，从特定的身体活动（如跑步）中识别和提取数据仍然具有挑战性。Objective.开发并验证一种算法，以识别手腕或躯干（腰部、臀部、胸部）佩戴设备的原始、自由活动加速度计数据中的跑步次数。Approach. CARL（连续振幅运行逻辑）分类器识别具有与运行一致的振幅和频率特征的加速度数据。CARL分类器在31名腰部和手腕上佩戴加速度计的成年人的数据上进行了训练，然后在来自30名新的、看不见的受试者以及来自使用不同设备、佩戴位置和样本频率的先前发布的数据集的166名受试者的自由生活数据上进行了验证。主要结果。在自由生活数据中，CARL分类器对于腰部数据的平均准确度（F-1评分）为0.984（95%置信区间0.962-0.996），对于手腕数据的平均准确度为0.994（95% CI 0.991-0.996）。在以前发布的数据集中，CARL分类器以平均准确度识别跑步（F-1评分）0.861（95% CI 0.836-0.884）胸部数据，0.911（95% CI 0.884-0.937），腰部数据为0.916（95% CI 0.877-0.948），手腕数据为0.870（95% CI 0.834-0.903）。错误分类主要发生在躯干加速度曲线与跑步相似的活动中，例如跳绳和使用椭圆机。意义CARL分类器可以准确地识别自由生活加速度计数据中短至三秒的跑步发作。CARL分类器的开源实现可在github.com/johnidavisiv/carl上获得。
Wearable accelerometers hold great promise for physical activity epidemiology and sports biomechanics. However, identifying and extracting data from specific physical activities, such as running, remains challenging. Objective. To develop and validate an algorithm to identify bouts of running in raw, free-living accelerometer data from devices worn at the wrist or torso (waist, hip, chest). Approach. The CARL (continuous amplitude running logistic) classifier identifies acceleration data with amplitude and frequency characteristics consistent with running. The CARL classifier was trained on data from 31 adults wearing accelerometers on the waist and wrist, then validated on free-living data from 30 new, unseen subjects plus 166 subjects from previously-published datasets using different devices, wear locations, and sample frequencies. Main results. On free-living data, the CARL classifier achieved mean accuracy (F-1 score) of 0.984 (95% confidence interval 0.962-0.996) for data from the waist and 0.994 (95% CI 0.991-0.996) for data from the wrist. In previously-published datasets, the CARL classifier identified running with mean accuracy (F-1 score) of 0.861 (95% CI 0.836-0.884) for data from the chest, 0.911(95% CI 0.884-0.937) for data from the hip, 0.916 (95% CI 0.877-0.948) for data from the waist, and 0.870 (95% CI 0.834-0.903) for data from the wrist. Misclassification primarily occurred during activities with similar torso acceleration profiles to running, such as rope jumping and elliptical machine use. Significance. The CARL classifier can accurately identify bouts of running as short as three seconds in free-living accelerometry data. An open-source implementation of the CARL classifier is available at github.com/johnidavisiv/carl.