Consumer markets demonstrate an observable trend towards mass customization. Assembly processes are required to adapt in order to meet the requirements of increased product complexity and constant variant updates. A concept to meet challenges within this trend, is a close collaboration between human workers and robots. Currently, in order to protect human operators, there are barriers and restrictions in place which prevent close collaboration. This is due to safety systems being mostly reactive, rather than anticipating motions or intentions. There are probabilistic models, which aim to overcome these limitations, yet predicting human behavior remains highly complex. Thus, it would be desirable to physically measure movement intentions in advance. A novel approach is presented of how upper-limb movement intentions can be measured with a mobile electroencephalogram (EEG). The human brain constantly analyses and evaluates motor movements up to 0.5 s before their execution. A safety system could therefore be enhanced to have an early warning of an upcoming movement. In order to classify the EEG-signals as fast as possible and to minimize fine-tuning efforts, a novel data processing methodology is introduced. This includes TimeSeriesKMeans labelling of movement intentions, which is then used to train a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The results suggested high detection accuracies and potential time gains of up to 513 ms to be achieved in a semi-online system. Thus, the time advantages included in a simulation demonstrated the potential to increase a system's reaction time and therefore improve the safety and the fluency of Human-Robot Collaboration.

消费市场显示出可大规模定制的趋势。为了适应增加产品复杂性和不断更新产品的要求，需要对组装过程进行调整。应对这一趋势中的挑战的概念是人类工人与机器人之间的紧密合作。当前，为了保护操作人员，存在阻碍紧密合作的障碍和限制。这是由于安全系统主要是被动的，而不是预期的动作或意图。有一些概率模型旨在克服这些限制，但预测人类行为仍然非常复杂。因此，期望预先物理地测量运动意图。提出了一种新颖的方法，说明如何使用移动式脑电图（EEG）测量上肢运动的意图。在执行之前，人脑会持续分析和评估运动时间，最长可达0.5 s。因此，可以增强安全系统以对即将发生的运动发出预警。为了尽可能快地对EEG信号进行分类并最小化微调工作，引入了一种新颖的数据处理方法。这包括运动意图的TimeSeriesKMeans标记，然后用于训练长期短期记忆循环神经网络（LSTM-RNN）。结果表明，在半在线系统中，检测精度很高，并且潜在的时间增益高达513 ms。因此，仿真中包含的时间优势证明了增加系统性能的潜力。