The 9-axis sensor of a smartphone measured the pseudo motion of chainsaw work to standardize the quantitative analysis of its work and extract information via clustering. A hidden Markov model could achieve high clustering accuracy between the felling and not-felling motion in engine-off and engine-on situations. New features (e.g., jerk, three-axis synthetic gyro acceleration, and the choice of the proper noise reduction parameter by moving average) were revealed. These features and parameters contribute to the robustness and high accuracy of clustering, revealing the hidden Markov model probabilities of automated information extraction from big raw data.

The ability to navigate a drone safely in forests located in non-Global Navigation Satellite System environments would enable construction of an in-forest three-dimensional (3D) point cloud using structure from motion (SfM), which promises to simplify and save on labor when performing forest surveys. This study aimed to elucidate about the potential for utilizing the Skydio 2 artificial intelligence-enabled drone for forest surveys. We evaluated the possibility of in-forest flights using the Skydio 2 and constructed an in-forest 3D point cloud using SfM processing. The drone could safely navigate the forest while automatically avoiding obstacles to prevent collisions with hanging branches and standing trees. SfM processing enabled the construction of a 3D point cloud with a terrestrial resolution of 0.0045 m using the ground control point (GCP) installed in the forest. We learned that the precision of the constructed in-forest 3D point cloud satisfied the regulation of precision requirements for public surveys. Taking the flight performance of the Skydio 2 into consideration, we would be able to operate the drone safely and utilize it for in-forest surveys, even in a forest with a stand density higher than that observed in this study. Because drones with in-forest flying abilities, such as the Skydio 2, are able to obtain in-forest information efficiently as high-precision data, they will serve as a useful tool for contributing to the realization of “Forestry DX (Digital Transformation)”.

New rigging methods were applied using synthetic fiber materials instead of wire rope for the guylines of tail trees relative to swing yarder to save labor and improve rigging efficiency in cable yarding. Synthetic fiber rope made from ultra-high molecular weight polyethylene and lashing belts made from high-strength polyester fiber were rigged and evaluated for effectiveness by assessing the heart rate of workers, work time, and tail tree swaying. The rigging equipment weight of the synthetic fiber method did not greatly decrease because a metal buckle was needed on each guyline. Heart rate was reduced by 30% in several tree workers due to the material's lightness, but the overall effect was unclear. Alternatively, the work time was reduced by approximately half. The swaying of the tail tree during yarding was larger than that of the wire rope. However, removing the structural elongation by retightening improved elasticity and reduced swaying of the tail tree. Using synthetic fiber materials for guylines can save labor and ensure work safety by appropriate tension management.

The gazing area and gazing target of an operator during spur road construction were analyzed using an eye camera. The gazing points were distributed toward the right side from the center of the visual field. Gaze constituted 32% of the observation time, and the remaining was saccades. This gaze occupation ratio was lower than that previously reported for driving cars and trains. In total, 67% of the gazing was distributed within the effective visual field. The targets that were the most gazed were bucket (and the periphery of bucket). The gazing frequency was 23.5% and the gazing time was 15.8%. However, these values were lower than those previously reported for excavation-turning-loading work on a flat plane using an excavator.