Effect of Training Parameters on the Ability of Artificial Neural Networks to Learn: A Simulation on Accelerometer Data for Task Recognition in Motor-Manual Felling and Processing

Abstract

Producing dynamic, real-time reliable data on the performance of timber harvesting operations has gained lately a lot of momentum due to the necessity to proactively manage the fleets of machines and machine allocation and to better monitor them in different operational environments to be able to understand their capability and performance. Techniques of Artificial Intelligence (AI) have been used recently to get accurate data at a low cost in many fields of science. In particular, the use of Artificial Neural Networks (ANN) has been proved to enhance classification accuracy in many applications. This work deals with the effect of factors that are typically used to set up an ANN on the performance of classification, by a case time-and-motion study implemented for motor-manual tasks under an experimental approach. A protocol was designed to vary the number of neurons in a hidden layer, number of iterations to train the ANN and the number of folds for cross-validation of data during training. The protocol was applied to a set of median-filtered vector-magnitude data collected at a 1Hz sampling rate by a triaxial accelerometer which was documented by a video approach to encompass five types of events. The results were promising, showing that it is possible to accurately classify the data by various performance metrics. The overall recall, for instance, may be as high as 98%. However, the number of iterations and neurons used to train the ANN are factors that significantly affect the classification performance. We conclude that implementing the ANN architectures to learn from filtered acceleration data has a lot of potential in longterm monitoring of motor-manual work and that future studies should be implemented to resemble the variation of operational conditions.

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

“…Fitness and sleep bands have been used (1) to monitor the physical activity and sleep patterns of forestry workers in order to understand how these factors may contribute to workplace hazards [89], and (2) to predict forestry worker fatigue by comparing heart rate and step count data to reaction and decision-making times [90]. Smartwatches paired with heart rate monitor chest straps have been used to evaluate workload associated with manual tree felling [91], while external accelerometers attached to machines have been used to develop ANNs that classify the activities of manually-driven bandsaws [92] and recognize activities associated with manual felling [93]. Preliminary activity recognition models have been developed for cable yarding work phases using a combination of smartphone sensor (global positioning system (GPS) and inertial measurement unit (IMU)) and camera data [66].…”

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

“…Most of the resource and process monitoring activities could benefit to a large extent from using the latest technologies of the artificial intelligence and machine learning to support real-time decision making and to set the ground for improvements. 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.…”

“…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 ].…”

“…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 ].…”