Construction Productivity and Ergonomic Assessment Using Mobile Sensors and Machine Learning

For a construction project to be on schedule and within budget, project managers must constantly monitor work progress, identify deviations from plans, and design a more efficient workplace. This often requires meticulous attention to how field tasks are conducted by workers over time. From a worker’s perspective, to be more productive may translate into carrying out tasks that exceed one’s natural physical limits. The sustained physical labor will result in work-related musculoskeletal disorders (WMSDs) which can adversely affect the project budget, schedule, and productivity. To prevent WMSDs, health and safety organizations have established rules and regulations which limit the duration and frequency of labor-intensive activities. Proper implementation of these standards requires that worker’s physical motions are constantly tracked. Monitoring workers’ activities serves a two-fold purpose: it provides information about the work progress, as well as help quantify the ergonomic risks associated with those activities. The complexity of activities, large number of workers, and psychological and physical barriers in the workplace make manual data collection very challenging. In this paper, the authors present and validate a model for remotely and unobtrusively monitor worker’s activities. The model deploys built-in smartphone sensors and machine learning algorithms to recognize worker’s activities in the field, and extract duration and frequency information, which will be ultimately used to evaluate productivity as well as ergonomic risks associated with each activity.