The research discussed in this paper is part of a project directed at increasing productivity in construction through mitigating the risk of Musculoskeletal Disorders (MSD). Postures and activities recognition through motion capturing techniques have shown promising potential for monitoring, assessing, and reducing such risks. Current motion sensing systems require a complex whole-body senor placement to capture and recognize construction activities, which limits the practicality and requires great computational effort. This challenge can be addressed through using a machine learning approach that recognizes specific activities from human motion data. The feasibility of reducing the computational effort through using fewer sensors rather than whole-body sensor placement was assessed through a case study. Five sensors were placed in targeted motion areas. The authors propose a novel automatic model configuration process to improve recognition performance under the selected sensor placement. It is based on designing optimal combination of data segmentation window size, feature sets, and classification algorithms for a specific set of injury-prone construction activities. The proposed approach achieved an average overall recognition accuracy of 0.81 and 0.74 for two sets of activities. The recognition model operation time is also reduced to less than 0.01 s under the proposed approach. In this initial case study, the model configuration process was developed iteratively based on the output from the test case. In subsequent efforts, the authors will develop a generic activity recognition model with predefined rules and criteria. This will further accelerate and automate the model configuration process.