# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 3 Methodologies
### Detecting Equipment Activity
#### 3.3.3 DCNN-Based Activity Recognitions  
In this study, a DCNN-based classifier was implemented to train the activity data of the classified equipment (Figure 5). The proposed DCNN model was designed to process a set of data provided by three pistons, equivalent to 10 s of activity (or 200 frames). It aims to analyze complex signal data (as though examining a single image) by extracting significant features from the dataset. In order to achieve this, the model was developed by the author without employing the backbone net architectures such as ResNet, MobileNet, or ShuffleNet. Figure 4: Data matrices for the percentage of each piston length. Figure 5: Example of the DCNN-based classifier for equipment activity data. In Figure 5, the distance values measured by each ToF sensor module are normalized to a value between 0 and 1 and are captured in a frame. An activity set comprises 200 such frames. The proposed DCNN model architecture consists of four main components (an input section, a feature extraction stage, a classification stage, and an output section). * Input section  
For this study, a 200 \(\times\) 3 matrix was input, and a total of 2070 samples were used. It consists of five participants, six activities for each individual, and 69 samples for each activity. * Feature extraction stage  
The feature extraction stage is composed of a convolutional block that varies from one to four steps. In our DCNN architecture, the feature extraction module comprises four sequentially arranged convolution blocks. Each block is strategically designed to enhance the network's ability to discern increasingly complex patterns within the input data. The configuration of these blocks is as follows:  
Convolution Block 1: This initiates the feature extraction process with a convolution-2D layer employing 3 \(\times\) 3 kernels and eight filters, incorporating the \"Padding\" of 1 pixel to maintain spatial dimensions. This is followed by a batch-normalization layer to normalize the activations and facilitate stable learning. The Relu layer introduces nonlinearity, allowing for the representation of complex functions. The block concludes with a max-pooling-2D layer (2 \(\times\) 2; stride = 1) to reduce feature map dimensions while preserving the most salient features. Convolution Block 2: This utilizes a convolution-2D layer with 3 \(\times\) 3 kernels, increasing the filter count to 16, to capture a wider array of features. A subsequent batch-normalization layer ensures the consistency of activations. The inclusion of a Relu layer maintains the network's capacity for nonlinear feature representation. Convolution Block 3: This further augments the network's feature detection capabilities with another Convolution-2D layer, with 32 filters this time. A batch-normalization layer follows for activation normalization. A Relu layer is used, again, to apply nonlinearity to the feature maps. Convolution Block 4: The final convolution block scales up the complexity with a Convolution-2D layer that utilizes 64 filters, enabling the network to capture the most abstract features. This is paired with a batch-normalization layer for maintaining activation consistency across batches. The block is completed with a Relu layer for nonlinear activation, which is crucial for complex pattern recognition. * Classification stage  
The classification stage directly addresses the task of assigning class probabilities to the input samples. The stage is structured as follows:  
Fully connected layer: The classification stage initiates with a fully connected layer comprising six neurons, each representing one of the potential class outcomes. This layer acts as a bridge, synthesizing the high-level features extracted by the preceding convolutional blocks into a format suitable for classification. The number of neurons in this layer corresponds to the total number of classes within the dataset, allowing for a dense representation of the learned features.