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Worker activity identification and classification is the most crucial and difficult stage in work sampling studies. Manual methods of recording are tedious and prone to error and, hence automating the task of observing and classifying worker activities is an important step towards improving the current practice. Very recently, accelerometer-based systems have been explored to automate activity recognition in construction, but it had been carried out in controlled environment. The purpose of this paper is to cover the evaluation of the system in field situations.

Experimental investigation was carried out on crews of iron workers and carpenters with accelerometer data loggers worn at selected locations on the human body. The accelerometer data collection was spread over a time period of two weeks, and video recording of the worker activities was concurrently carried out to serve as ground truth, the reference used for comparison. The activity recognition analysis was carried out on accelerometer data features using a decision tree algorithm.

It was found that the classification using the individual training scheme performed better when compared with the collective training scheme for both the trades. The field studies results showed that the classification accuracies for iron work and carpentry are 90.07 and 77.74 per cent, respectively, using decision tree classifier. It was found that similarities of movements were a major cause for lower accuracy of recognition.

The work being preliminary in nature has used the basic classifier and pre-processing methods and, standard settings of algorithms.

The paper has investigated accelerometer-based method for construction labour activity classification in field situations.