High occupational physical activity is associated with lower health. Shoe-based movement sensors can provide an objective measurement of occupational physical activity in a lab setting but the performance of such methods in a free-living environment have not been investigated. The aim of the current study was to investigate the feasibility and accuracy of shoe sensor-based activity classification in an industrial work setting. An initial calibration part was performed with 35 subjects who performed different workplace activities in a structured lab setting while the movement was measured by a shoe-sensor. Three different machine-learning models (random forest (RF), support vector machine and k-nearest neighbour) were trained to classify activities using the collected lab data. In a second validation part, 29 industry workers were followed at work while an observer noted their activities and the movement was captured with a shoe-based movement sensor. The performance of the trained classification models were validated using the free-living workplace data. The RF classifier consistently outperformed the other models with a substantial difference in in the free-living validation. The accuracy of the initial RF classifier was 83% in the lab setting and 43% in the free-living validation. After combining activities that was difficult to discriminate the accuracy increased to 96 and 71% in the lab and free-living setting respectively. In the free-living part, 99% of the collected samples either consisted of stationary activities or walking. Walking and stationary activities can be classified with high accuracy from a shoe-based movement sensor in a free-living occupational setting. The distribution of activities at the workplace should be considered when validating activity classification models in a free-living setting.

中文翻译：

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基于鞋的运动传感器的工作场所活动分类。

高职业体育活动与较低的健康有关。基于鞋的运动传感器可以在实验室环境中提供对职业体育活动的客观测量，但尚未研究此类方法在自由生活环境中的性能。当前研究的目的是调查基于鞋传感器的活动分类在工业工作环境中的可行性和准确性。初始校准部分由 35 名受试者进行，他们在结构化实验室环境中进行不同的工作场所活动，同时通过鞋传感器测量运动。训练了三种不同的机器学习模型（随机森林 (RF)、支持向量机和 k 最近邻），以使用收集的实验室数据对活动进行分类。在第二个验证部分，29 名行业工人在工作中被跟踪，同时一名观察员注意到他们的活动，并使用基于鞋的运动传感器捕捉到他们的运动。使用自由生活工作场所数据验证了训练分类模型的性能。RF 分类器始终优于其他模型，在自由生活验证方面存在显着差异。初始 RF 分类器的准确率在实验室环境中为 83%，在自由生活验证中为 43%。在结合难以区分的活动后，在实验室和自由生活环境中的准确率分别提高到 96% 和 71%。在自由生活部分，99% 的收集样本要么包括静止活动，要么包括步行。在自由生活的职业环境中，可以通过基于鞋的运动传感器对步行和静止活动进行高精度分类。在自由生活环境中验证活动分类模型时，应考虑工作场所的活动分布。