# 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
### Audio-based  
The primary use of audio-based recognition for activity detection of construction workers has been captured through either a single microphone or an array of microphones placed on construction sites [73]. [PERSON] and [PERSON] [38] were able to sort four activities (table-saw cutting, nailing with a nail gun, drilling, and hammering) in a modular construction factory environment. SVM was primarily used extensively in this method since it was required to train numerous classification models to perform a comparative analysis of various feature sets and window size combinations [38]. However, research in audio-based approaches for measuring or capturing CLP is very limited, as this approach is only feasible when the workers' activities generate distinctive sounds. A combination with other action recognition approaches should be explored for the quantification of productivity data (e.g., cycle time and productive time). To summarize the measurement of productivity in CLP, several different modalities and methods have been discussed. The majority of the body of knowledge focuses on clearly defined activities that vary among each of the three methods. Despite the convenience of audio-based methods, the applicability is severely limited as they are only applicable to activities with unique sound patterns. This limits the practical implementation in a generalized model. On the other hand, computer vision needs the greatest data storage capacity, and the cameras are generally installed at a stationary position where they need to remain physically non-intrusive. Therefore, to monitor multiple or all the workers of the entire construction site would require extensive camera setups that might lead to high costs. This is deemed necessary in order to replace manual CLP monitoring (e.g., work sampling). Thus, kinematics-based approaches seem to be the optimal option of the three techniques. On the other hand, the limitations of kinematic-based approaches shall not be overlooked as well. This technique of gathering data is the only one of the three that requires physical intrusion, and it is also the most manipulable. Nevertheless, it is observed that most studies were carried out using a limited sample of construction workers and did not address complicated construction working sites. The Automated Approach of the Measurement of Construction Equipment Productivity  
Construction equipment is considered a principal element of construction production as well; therefore, its operation must be measured and examined methodically in order to track project productivity. Generally, given that various construction tasks are repetitive and that the number of cycles in a given amount of time often determines the production rate, CEP measurement of production rates depends heavily on the cycle time. Construction heavy equipment often performs repetitive tasks as cyclical operations; therefore, recognizing those cyclic actions or activities for cycle times is considered vital for scheduling and productivity analysis in construction projects [12]. Similarly, with CLP, automated CEP measurement of productivity relies on three broad categories of sensor modalities (kinematic-based, vision-based, and audio-based) as well. Kinematic-based  
Generally, construction equipment generates distinct kinematic signals when they are in operation. These signals are acceleration, angular velocity, velocity, and orientation data. There are adoptions of location-based sensors (e.g., UWB, GPS, and RFID) and motion-based sensors (e.g., accelerometers and IMUs). However, the ability to provide detailed analysis of production cycles and track stationary activities of construction equipment is not within the capacity of location-based methods [74]. Therefore, researchers tend to focus on utilizing IMUs for activity recognition. Predominantly, the characteristics and the volume of the data reflects highly on the selection of the learning algorithm [75; 76].