## 5 Activity Recognition  
### Deep Learning Models  
[PERSON] et al. [29] showed three DL models for activity recognition: (1) activity recognition via telematics data is a solid alternative to the existing motion-based method; (2) recurrent neural network (RNN) models consisting of long-short term memory (LSTM) consider temporal dependencies in the modeling, which is most suitable for this use case; and (3) adding convolutional neural network (CNN) enables good feature extraction, outlier filtering, and smoothing method. Their proposed hybrid models on RNN and CNN, the DeepConvLSTM, and the bidirectional variant DeepConvBiLSTM were also developed and tested on the use case of Rosenheim. An average accuracy of up to 96.1% for 27 activities was achieved. However, the question arises on the generalization capability of the results. The dataset was split randomly using the Scikit-Learn function in Python: 56% training, 14% validation, and 30% test data. Regarding the relatively low frequency and slow state changes in the equipment motions, two observable samples can be similar but are located once in the training set and once in the test set. Studies on addressing this issue were conducted in the previous work of [PERSON] [41]. The dataset was, therefore, coherently split by production days. The results based on this data splitting methodology led to a decrease in the average accuracy of up to 51.9%. One reason for this result can be the high complexity of the data modeling recognizing 27 activities. As mentioned, comparable studies in the literature only examine activities of less than 10 (LoD9). In this paper, we, therefore, investigate the influence on data modeling by hierarchical classification according to [PERSON] et al. [30]. Table 2 shows the DL models implemented and tested by [PERSON] [41]. The DL models were implemented using the Tensorflow package in Python. The baseline model extends an ANN with only one layer (based on previous work from the authors [14; 41]) to a multilayer perceptron (MLP) consisting of 5 dense layers with 128 neurons each. While this model does not account for temporal dependencies, it is used as a comparison to investigate the influence of temporal relationships in activity recognition. RNN is used to consider the temporal dependencies of the data, providing the outputs of the previous layer and the outputs of its layer at the last time. One of the significant types of RNNs is the LSTM network, which was also used by [PERSON] and [PERSON] [11]. In addition to the inputs that are present in simple RNNs, LSTMs have an additional long-term state, which can store long-term dependencies [42]. The proposed hybrid framework for human and construction machinery activity recognition combines the short-time feature extraction capabilities of convolutional layers with the long-time temporal dependencies modeling capabilities of LSTM layers. The original architecture developed by [PERSON] and [PERSON] [43] consists of one input layer, four convolutional layers, two LSTM layers, and one Softmax activation layer. A slightly modified version of this architecture is used by [PERSON] et al. [12]. They add batch normalization layers between the convolutional layers and a dropout layer between the convolutional and recurrent layers. The unidirectional model used in this paper is based on this modified architecture with minor changes to reduce overfitting. In addition, we investigate the influence of a bidirectional architecture, which processes the inputs in both directions. Bidirectional \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MLP** & **DeepConvLSTM** & **DeepConvBiLSTM** \\ \hline Architecture & 5 xDense-Softmax & 3 xCNN–2 xlSTM-Softmax & 3 xCNN–2 xBiLSTM-Softmax \\ Temporal window & no & Overlapping sliding window & Overlapping sliding window \\ Window size & - & 16 s & 16 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Architecture of the DL models used for the parameter study. RNNs have numerous applications in the field of Natural Language Processing (NLP) [44].