Predicting Driver's mental workload using physiological signals: A functional data analysis approach.

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

Optimising computer vision-based ergonomic assessments: sensitivity to camera position and monocular 3D pose model.

Numerous computer vision algorithms have been developed to automate posture analysis and enhance the efficiency and accuracy of ergonomic evaluations. However, the most effective algorithm for conducting ergonomic assessments remains uncertain. Therefore, the aim of this study was to identify the optimal camera position and monocular 3D pose model that would facilitate precise and efficient ergonomic evaluations. We evaluated and compared four currently available computer vision algorithms: Mediapipe BlazePose, VideoPose3D, 3D-pose-baseline, and PSTMO to determine the most suitable model for conducting ergonomic assessments. Based on the findings, the side camera position yielded the lowest Mean Absolute Error (MAE) across static, dynamic, and combined tasks. This positioning proved to be the most reliable for ergonomic assessments. Additionally, VP3D_FB demonstrated superior performance among evaluated models.Practitioner Summary: This study aimed to determine the most effective computer vision algorithm and camera position for precise and efficient ergonomic evaluations. Evaluating four algorithms, we found that the side camera position with VideoPose3D yielded the lowest Mean Absolute Error (MAE), ensuring precise and efficient evaluations.

Cross-cultural comparison of body dimensions and trends: an analysis of ANSUR and Size Korea datasets for automotive seating design.

The purpose of this study is to investigate the change in body dimensions over time in both Western (US) and Eastern (Korea) populations. In order to analyse the change of body dimension between the past and present and between western and eastern population, 13 body dimensions relating to automobile driver seat design were extracted from the ANSUR and Size Korea datasets at two time points, the past (ANSUR I: 1988, Size Korea: 1992) and the present (ANSUR II: 2012, Size Korea: 2012). Most of the dimensions differed significantly between past and present, as well as between the US and Korea. Overall, the data show an increasing trend of body dimensions over time for both genders. Based on the results, all countries should be encouraged to conduct periodic and national anthropometric research because body dimensions are continuously changing over time worldwide.Practitioner summary: This paper describes a study that investigates the changes in body dimensions over time in Western (US) and Eastern (Korean) populations. Findings indicate increasing trends in most dimensions for both populations, crucial for user-friendly product design and preventing hazards caused by faulty products.

Machine learning approach for study on subway passenger flow.

We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport. Then, we forecast the daily number of passengers with respect to each cluster. By comparing our predicted results with the actual number of passengers, we demonstrate the predicted number of passengers based on the clustering results is more accurate in contrast to the result without considering the regional properties. The result from our data-driven approach can be applied to improve the subway service plan and relieve infectious diseases as we can reduce the congestion by controlling train intervals based on the passenger flow. Furthermore, the prediction result can be utilized to plan a 'smart city' which seeks shorter commuting time, comfortable ridership, and environmental sustainability.

Development and evaluation of attachable anti-vibration handle.

Blueberry production has skyrocketed in the past two decades due to an exponential increase in consumer demand around the world. Hand harvesters are used, avoiding damage to the fruit and increasing harvesting efficiency multifold when compared with that of hand-picking. The downside of these hand harvesters is their high Hand Arm Vibration (HAV), which is very dangerous for the worker and can cause hand-arm vibration syndrome (HAVS). The aim of this study is to propose a spring-based anti-vibration handle that can be attached to vibrating equipment (blueberry hand harvester). Four different parameters were measured for the developed spring-based handles: hand arm vibration, wrist posture, muscle activity, and subjective discomfort rating. Results have shown that the use of a spring-based handle can reduce HAV by 61.1%, which is within the exposure limit values (ELV) defined by the European Union.