Applied Ergonomics最新文献

With advances in autonomous vehicle technology and in-cabin occupant monitoring systems, prediction of motion sickness (MS) has emerged as a key challenge to improve passenger experience. In this paper, a framework for MS prediction is proposed leveraging classification algorithms and timeseries physiological data, including blood volume pulse, electrodermal activity, and neck surface electromyography. The dataset used for model training contains over 1500 min of in-vehicle data, three test conditions, and a range of subject demographics. Model predictions were able to achieve 81% accuracy for binary classification (sick or not sick) and 58% for ternary classification (low, moderate or high sickness). In addition, feature importance analysis identified electrodermal activity and surface electromyography as the most relevant data streams for MS prediction. Finally, the paper analyzed the temporal dependency of physiological data on MS response and found that physiological data can precede a subject's self-reporting of MS by up to 180 s.

Passive back-support exoskeletons (BSEs) are promising but underexplored interventions to reduce the high physical demands of roofing shingling. Eighteen participants performed simulations of shingle installation tasks under 12 different conditions. These conditions included all combinations of three BSE levels (Rigid, Soft, and no BSE), two task orientations (peak-facing vs. side-facing), and two roof slopes (18° vs. 26°). Using the rigid BSE significantly reduced lumbar muscle activation (11-17%) compared to no BSE, without altering trunk flexion. In contrast, the soft BSE reduced trunk flexion (∼4%) without altering lumbar muscle activation. Both BSEs reduced perceived low back exertion (∼16%); however, the rigid BSE increased leg discomfort (∼26%), and the soft BSE increased shoulder exertion (∼19%). Our results suggest that using BSEs can be beneficial for shingle installation tasks but also highlight the importance of considering device-specific biomechanical benefits and associated trade-offs to ensure effective application.

This study developed a taxonomic approach based on 30 technical failure occurrences between 2002 and 2020. This framework extends the author's previous work on human-machine-environment-procedure (HMEP) taxonomy through the application of Rasmussen's Risk Management Framework (RRMF), which can serve as a common framework to aggregate or compare datasets collected by different organizations. This RRMF-based HMEP taxonomy was used to categorize the contributing factors of 30 technical failure occurrences into seven mutually exclusive categories and respective subcategories for statistical analysis and other risk management analysis. The RRMF provided a hierarchical structure for these first-layer categories, including government oversight, manufacturer deficiency, company management, company procedure and documentation, people and activity management, technical failure, and environment. The functional block diagram and failure modes and effects analysis (FMEA) were applied to translate the technical failures of 12 environment control system failure cases into FMEA tabular statements. Several failure modes had been identified for environmental control system failure occurrences, e.g., sense line leakage, flawed sensor input, duct crack and leakage, broken part (pin and spring), seal degradation, contamination, tube rupture, mechanical joining defect, poor contact, and unanticipated failure instances for electronic centralized aircraft monitor (ECAM). Each failure mode can provide diagnostic information to identify and fix the environmental control system failure problem more effectively. In conclusion, this RRMF-based HMEP taxonomy provided a mental model to guide the data collection during an accident investigation and subsequently derive accident patterns as the core for Safety Management System (SMS) implementation.

Recent advances in human pose estimation (HPE) have enabled markerless motion capture (MoCap) techniques as a promising alternative to traditional marker-based MoCap systems. However, most HPE algorithms only provide sparse video keypoints, which are insufficient to estimate joint angles in all anatomical planes according to biomechanical guidelines. OpenCap, an open-source smartphone-based markerless MoCap platform, addresses this limitation using a deep learning model (named the marker augmenter) that predicts dense anatomical markers from sparse video keypoints. However, it has shown lower performance for activities not included in its training dataset, such as occupational lifting tasks. In this study, we adapted the original marker augmentation model of OpenCap and proposed a task-specific model for occupational lifting, trained on a large and diverse dataset of manual lifting tasks. The proposed model reduced both kinematic errors (mean RMSE = 9.45° vs. 15.04°) and error variability (SD = 7.26° vs. 16.13°) compared to the original model. These findings suggest that OpenCap can be adapted for occupational lifting tasks, offering a low-cost, easy-to-use, and field-viable solution to collect 3D lifting kinematics for ergonomics applications.