Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.

中文翻译：

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通过分析-建模-识别算法和上下文标签识别工业 4.0 场景中的人类活动

活动识别技术仅在受控条件下表现出良好的性能，其中允许的动作数量有限。相反，工业应用是具有真实和不受控制的条件的场景，在这些场景中可能会开发出数千种不同的活动（例如运输或制造工艺产品），并且具有令人难以置信的可变性。在这种情况下，需要新的和增强的人类活动识别技术。因此，在本文中，提出了一种新的活动识别技术，专注于工业 4.0 场景。提议的机制由不同的步骤组成，包括第一个分析阶段，其中使用移动平均、滤波器和信号处理技术处理物理信号，以及集成了动态时间扭曲技术和 k 最近邻解决方案的原子识别步骤；第二阶段，使用广义马尔可夫模型对活动进行建模，并使用多层感知器识别上下文标签；第三步，使用先前创建的马尔可夫模型和上下文信息识别活动，格式为标签。所提出的解决方案实现了 87% 的最佳识别率，这证明了所描述方法的有效性。据报道，与最先进的解决方案相比，性能提高了 10%。第三步，使用先前创建的马尔可夫模型和上下文信息识别活动，格式为标签。所提出的解决方案实现了 87% 的最佳识别率，这证明了所描述方法的有效性。据报道，与最先进的解决方案相比，性能提高了 10%。第三步，使用先前创建的马尔可夫模型和上下文信息识别活动，格式为标签。所提出的解决方案实现了 87% 的最佳识别率，这证明了所描述方法的有效性。据报道，与最先进的解决方案相比，性能提高了 10%。