# 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
### Kinematic-based  
The rapid development of body-worn sensors (e.g., accelerometers and Inertial Measurement Units (IMUs)) and smartphones has enabled the detection of motion associated with workers' activities. Location-based sensing devices (e.g., Radio Frequency Identification (RFID), Ultra-Wideband (UWB), and Global Position System (GPS)) are commonly explored by researchers as well, but they are not suitable for detailed activity monitoring for workers, as the location information is insufficient to distinguish between the different activities carried out by workers in the same position [67,68]. Generally, the execution of any activities will allow humans to initiate certain postures or actions which are defined as motion [62]. The measurement of workers' posture and motions during various construction activities is conducted via the body-worn sensors. They collect data in the form of acceleration, velocity, and orientation. Due to the need for the attachment of sensors to workers' bodies, their working performance might be impacted despite the sensors being low-cost and offering automated data acquisition [2]. Recognition accuracy is dependent on the quantity of the sensors and the placement of the sensors. Multiple sensors or single sensors have been explored to capture labor motions for the dataset. A motion dataset regarding the material handling activities (placing, lifting, carrying,) of laborers was collected via a set of motion tracking devices (17 IMUs), a commercial product named Perception Neuron [62]. In addition, acceleration data was collected via a motion node (GLI Interactive LLC, Seattle) single accelerometer for the study masonry of works [37]. The optimum placement of sensors on laborers for masonry works was explored by [PERSON] et al. [68] as well. The waist has often been regarded as a promising position [37] due to the placement where the whole-body mass is at the center, and the primary body motion [69] is better represented by the sensor signals from the waist. Still, this would not be the case if the activities are hand- or arm-dominant, as it would be difficult to differentiate the actions. This was proven when the wrist-worn accelerometer achieved higher accuracy [68] compared to the waist-worn sensor [37]. Due to the nature of classifying activities from kinematic-based sensing technologies being considered data mining, the training of data generated via ML algorithms are called classifiers [37]. By identification and classification of the actions, the productive time of the activity can then be identified. Past scholarly works have demonstrated the processing of these extensive datasets is performed via ML algorithms to achieve automated action recognition. The evaluation of five different classification techniques (support vector machine (SVM), decision tree, k-nearest neighbor (k-NN), artificial neural network (ANN), and logistic regression) was conducted, where k-NN was highly superior in terms of accuracy and even computational time [14]. However, in [PERSON] et al. 's [68] research, SVM produced slightly better performance among k-NN, decision Tree, and ANN techniques. In addition, the utilization of a stratified 10-fold cross-validation for the selection of the right classifier model of the three algorithms (decision trees, multilayer perceptron, and naive Bayes) was conducted, where multilayer perceptron emerged superior to others for recognizing activities in masonry [37]. Due to the variability of movement among workers, multiple acceleration signal patterns could be produced, including those that are performing the same actions. This would lead to a decrease in classification accuracy [68]. Therefore, a larger volume of data is required to be trained and tested concurrently with multiple algorithms to achieve the best accuracy. Thus, the majority of the researchers adopted multiple algorithms and sample sizes and conducted hyperparameter tuning to gauge their accuracy, enabling them to select the optimum classifier rather than just opting for one.