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首页> 外文期刊>IEEE sensors journal >Unobtrusive and Energy-Efficient Swimming Exercise Tracking Using On-Node Processing
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Unobtrusive and Energy-Efficient Swimming Exercise Tracking Using On-Node Processing

机译：使用节点处理技术进行不显眼且节能的游泳运动跟踪

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摘要

Body-worn sensors for movement analysis in swimming have to be unobtrusive and energy-efficient. We present a swimming exercise tracker for the unobtrusive positioning at the back of the head and an energy-efficient analysis using an on-node implementation. To develop the system, we collected head kinematics from 11 subjects in two 200-m medley races comprising breaks, turns, and four swimming styles. Each subject was equipped with a 6-D inertial measurement unit and completed one session in rested and fatigued state. Data were analyzed with a classification system, whereby different classifiers, window sizes, and feature sets were evaluated. Algorithm selection for on-node processing was performed on the basis of classifier accuracy and computational cost. The algorithm with the best tradeoff in accuracy and computational cost was selected and had a classification rate of 85.4%. Energy consumption of both on-node processing and Bluetooth streaming was evaluated on the Shimmer sensor platform. The results revealed energy savings of over 60% when data were processed on the sensor node. The presented analysis approach can be easily applied to other data analysis tasks, and the presented toolchain can support the rapid development of wearable systems in sports and healthcare.
机译：用于游泳运动分析的穿戴式传感器必须不引人注目且节能。我们提供了一种游泳运动跟踪器，用于在头部后部进行不显眼的定位，并使用节点上的实现方式进行节能分析。为了开发该系统，我们在两次200米混合泳比赛中收集了来自11个受试者的头部运动学信息，包括休息，转弯和四种游泳风格。每个受试者都配备了6维惯性测量单元，并在休息和疲劳状态下完成了一个疗程。使用分类系统分析数据，从而评估不同的分类器，窗口大小和特征集。节点处理的算法选择是基于分类器的准确性和计算成本进行的。选择了在准确性和计算成本上具有最佳折衷的算法，其分类率为85.4％。在Shimmer传感器平台上评估了节点处理和蓝牙流传输的能耗。结果表明，在传感器节点上处理数据时，可节省60％以上的能源。提出的分析方法可以轻松地应用于其他数据分析任务，并且提出的工具链可以支持体育和医疗保健中可穿戴系统的快速发展。

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