# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
### Dataset Acquisition
#### 4.1.2 The Automated Approach to the Measurement of Construction Productivity  
Compared to the traditional approaches (e.g., direct observations and survey-based methods), developments in data collection automation to monitor resources and measure the progress of activities have shown encouraging potential [14]. [PERSON] and [PERSON] [62] mentioned that given construction tasks naturally consist of repetitive physical activities, automating the identification of workers' movements can aid in measuring productivity. Several efforts have been made to develop automated dataset collection for both labor productivity and equipment productivity, mainly vision-based, kinematic-based, and audio-based approaches [12; 63; 64; 65]. Generally, previous studies adopted automated dataset collection, which was then analyzed by ML algorithms to extract productivity data. This is because the dataset is in the form of pure raw data (images, videos, sounds, accelerations, velocities, orientation data, and location data), which would need to be processed. For instance, each dataset would need to undergo specific data preparation steps (i.e., data segmentation, feature extraction, feature selection, and classification) to acquire the productivity data, unlike the traditional approach, which is much more straightforward, as discussed earlier. Additionally, to enhance the learning capacity and prediction performance of ML algorithms, data preparation is essential, where the acquired data should be sorted and brought to conformance [3; 66]. Figure 11 demonstrates the overview of the automated approach, where each type of productivity and sensor will be reviewed further.  
The Automated Approach to the Measurement of Construction Labor Productivity  
During the last few decades, the measurement of the productivity of labor activity has primarily leaned on traditional methods. With the advancement of technologies, recognizing labor activity can be done via three major sensing technologies (i.e., vision-based, audio-based, and kinematic-based). Subsequently, the incorporation of classification  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Study** & **Types of Traditional Method** & **Type of Device** & **Type of Productivity Input** & **Strengths** & **Limitations** \\ \hline
[3] Labor & Daily Record and & Data Sheets, & Work activities, number of & & \\ Productivity & Interview & Structured Interview & done, and number of hours & & \\ \hline
[24; 43] Equipment & Interview and & Structured & Cycle time, soil & & \\ Productivity & Questionnaire & Interview and & characteristics, and number & & \\ \hline
[53] Labor & Interview & Structured & Construction working hours, & & \\ Productivity & Interview & Interview & construction workers, and & & \\ \hline
[55] Labor & Interview and & Structured & Work activities, quantity of & & \\ Productivity & Questionnaire & Interview and & work done, number of days, & & \\ Survey & Questionaires & number of labores per day, & & & \\  & & & and exposure conditions. & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 11: Overview of the automated approach to dataset acquisition.  
algorithms using ML or DL for action recognition have been used for the generation of productivity data.