Abstract
Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.
Highlights
The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions
Cable logging operations consist of felling, yarding, processing, and loading work phases [1], with the yarding phase often characterized as six distinct cycle elements [2]
Mean work activity cycle times for the chaser were generally shorter than the mean cycle times for the choker setter, and the maximum cycle times tended to be slightly longer for the choker setter activities compared to the chaser activities (Table 2)
Summary
Cable logging operations consist of felling, yarding, processing, and loading work phases [1], with the yarding phase often characterized as six distinct cycle elements (outhaul, lateral out, hookup, lateral in, inhaul, and unhook) [2]. Logging workers are often fatally injured when struck by objects such as falling trees, limbs, or machines [4,5,6,7,8,9,10]. In an analysis of cable logging accidents, Tsioras et al [11] found that broken spar and anchor trees, bouncing cables, and falling objects contributed to the majority of accidents and most incidents occurred when workers were struck by or struck against an object. While the increased mechanization of logging has generally led to a decrease in injury rates, ground crew members working alongside machines, rigging crew workers, and hand fallers are still at risk [4, 6, 8, 12] and may benefit from the use of a variety of positioning and wearable sensor-based technologies that increase situational awareness and reduce accidents
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