Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach.
Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach.
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DOI：
10.1371/journal.pone.0261765
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
电气线路工人(ELW)经历着恶劣的环境，其特点是长时间轮班、远程操作和潜在的危险任务。可穿戴设备提供了一种对生产力和安全性进行不引人注目的监控的机会。监测的先决条件是自动识别正在执行的任务。人类活动识别已被广泛应用于日常生活活动的分类。然而，由于任务的多样性和复杂性，文献仅限于电力线路维护/维修任务。我们研究了如何从单个手腕佩戴的加速度计中设计特征，以对ELW任务进行分类。具体地说，研究了三个特征集(时间、频率和时频)和两个窗口长度(4秒和10秒)上的三个分类器，以识别十个常见的ELW任务。根据实验室环境中37名参与者的数据，评估了两种应用场景：(A)受试者内，为每个工人培训和部署个性化模型；(B)受试者之间，汇集数据以培训可为新工人部署的通用模型。两种方案的准确率≥都达到了93%，10秒窗口的准确率提高到了≥96%。计算了总体和特定类别的特征重要性，并解释了这些特征对所获得的预测的影响。这项工作将有助于未来使用可穿戴设备的ELW的风险缓解。
Electrical line workers (ELWs) experience harsh environments, characterized by long shifts, remote operations, and potentially risky tasks. Wearables present an opportunity for unobtrusive monitoring of productivity and safety. A prerequisite to monitoring is the automated identification of the tasks being performed. Human activity recognition has been widely used for classification for activities of daily living. However, the literature is limited for electrical line maintenance/repair tasks due to task variety and complexity. We investigated how features can be engineered from a single wrist-worn accelerometer for the purpose of classifying ELW tasks. Specifically, three classifiers were investigated across three feature sets (time, frequency, and time-frequency) and two window lengths (4 and 10 seconds) to identify ten common ELW tasks. Based on data from 37 participants in a lab environment, two application scenarios were evaluated: (a) intra-subject, where individualized models were trained and deployed for each worker; and (b) inter-subject, where data was pooled to train a general model that can be deployed for new workers. Accuracies ≥ 93% were achieved for both scenarios, and increased to ≥96% with 10-second windows. Overall and class-specific feature importance were computed, and the impact of those features on the obtained predictions were explained. This work will contribute to the future risk mitigation of ELWs using wearables.
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影响因子：
3.2
作者：
Hajifar, Sahand;Sun, Hongyue;Cavuoto, Lora A.
通讯作者：
Cavuoto, Lora A.
DOI：
10.3390/s140406474
发表时间：
2014-04-09
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
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通讯作者：
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DOI：
10.1061/(asce)co.1943-7862.0001849
发表时间：
2020-07-01
影响因子：
5.1
作者：
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通讯作者：
Xing, Xuejiao
DOI：
10.1061/(asce)cp.1943-5487.0000097
发表时间：
2011-09-01
影响因子：
6.9
作者：
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通讯作者：
Varghese, Koshy
影响因子：
2.4
作者：
ANTONSSON, EK;MANN, RW
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MANN, RW