Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations

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.

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

“…While no studies were identified to evaluate the industry’s needs for such technologies in Romania, some small-scale tests have already proven their usefulness, in terms of cost saving and safety [ 8 , 9 , 10 ]. In addition, the operational level has been identified in international forestry to be one of the potential beneficiaries of sensor-based and machine learning implementations [ 8 , 9 , 10 , 11 , 12 ], which enabled significant resource savings and safety improvements. At this level, manual-dominated tasks have been approached in forestry under the umbrella of the so-called human activity recognition, which has been implemented by the use of various data collection platforms and machine learning techniques e.g., [ 11 , 12 ].…”

“…In addition, the operational level has been identified in international forestry to be one of the potential beneficiaries of sensor-based and machine learning implementations [ 8 , 9 , 10 , 11 , 12 ], which enabled significant resource savings and safety improvements. At this level, manual-dominated tasks have been approached in forestry under the umbrella of the so-called human activity recognition, which has been implemented by the use of various data collection platforms and machine learning techniques e.g., [ 11 , 12 ]. A similar approach has been used to monitor tool or machine-supported tasks, at least when such machines were not equipped with built-in production monitoring systems [ 8 , 9 , 10 , 13 ]; this approach still justifies its relevance due to the low to intermediary mechanization level of forest operations that still prevails in many parts of the world [ 14 , 15 , 16 ].…”

“…The fact that a human cannot perform the tasks of a machine in a natural way makes AI a complement to global development. It is likely, therefore, that artificial intelligence will soon be present in every aspect of our life since it can be already found in a wide range of industries, including medicine [ 20 , 21 , 22 , 23 ], communication [ 24 , 25 , 26 , 27 ], marketing [ 28 , 29 , 30 ], agriculture [ 31 , 32 , 33 ] and, of course, forestry [ 8 , 9 , 10 , 11 , 12 , 13 , 34 , 35 ]. In relation to operational monitoring by the means of automated time studies, recent research on the topic in forestry have proven that high classification accuracies may be achieved by the use as inputs in the machine-learning algorithms of raw signals outputted by various type of sensors such as accelerometers, gyroscopes and sound-pressure level meters [ 8 , 9 , 10 , 11 , 12 , 13 ].…”

“…It is likely, therefore, that artificial intelligence will soon be present in every aspect of our life since it can be already found in a wide range of industries, including medicine [ 20 , 21 , 22 , 23 ], communication [ 24 , 25 , 26 , 27 ], marketing [ 28 , 29 , 30 ], agriculture [ 31 , 32 , 33 ] and, of course, forestry [ 8 , 9 , 10 , 11 , 12 , 13 , 34 , 35 ]. In relation to operational monitoring by the means of automated time studies, recent research on the topic in forestry have proven that high classification accuracies may be achieved by the use as inputs in the machine-learning algorithms of raw signals outputted by various type of sensors such as accelerometers, gyroscopes and sound-pressure level meters [ 8 , 9 , 10 , 11 , 12 , 13 ]. In addition, signals outputted by accelerometers coupled with ML techniques have proven very useful not only in the forestry but also in other engineering disciplines such as those dealing with infrastructure and its monitoring [ 36 , 37 , 38 ].…”

A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations

“…Using a smartphone secured to the belt of a timber faller, the activity recognition model characterized manual felling work elements and delay with accuracies between 65.9% and 99.6%. More recently, smartwatch sensors have been successfully used to develop activity recognition models for rigging crew workers setting and disconnecting log chokers on cable logging operations [ 57 ]. These new smart device-based activity recognition applications show promise for further integration into forest industries.…”