Variations of Motions and Spatial Interpretation for Action Recognition

The experimental results confirm that the proposed action recognition can produce reliable and promising outcomes. Yet, further research efforts are necessary to address issues with motion variations and the spatial context of situations for the improvement of motion analysis in a field setting.

1. Variations of Motions: Movement patterns when people take actions vary from time to time as well as from individual to individual; that is, even when people intend to take the same action, variations in motion always exist. For instance, when carrying a tool up a ladder, one person might use their right hand to hold the object and their left hand to grasp a rung, while another person might use their right hand to grasp a rail and their left hand to carry the tool. To ensure that these variations in movement patterns are reflected in action recognition, future research will investigate and further advance emerging data analytical methods based on existing algorithms such as GPDM (Wang et al. 2008) and Bayesian Network Model (BNM) (Ma et al. 2010); GPDM optimizes variable trajectories of action samples in a low-dimension space to be close to the average value of the trajectories over time, while BNM attempts to parameterize variations on motions and describes the relationship between motion styles and the variations. These approaches will thus help address such issues by modeling and parameterizing movement patterns and styles in motion data analysis.

2. Spatial Interpretation: In this study, the proposed action recognition approach relies solely on action and posture analysis. The spatial interpretation of actions, however, allows for the contextual understanding of workers’ behavior as well as for the precise detection of unsafe actions. For instance, a reaching-far action, which served as an example of at-risk behavior in this research, may not be regarded as unsafe if taken at a low elevation. This exemplifies that the definition of at-risk behavior may often, particularly in the case of falls, involve the spatial restraints. Future studies will thus examine both the extraction of the geometrical information of a human as well as a device (e.g., a ladder) from motion capture data, and the data fusion approach to action recognition combining motion data with spatial-temporal positions of a body.

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