The present study developed a sizing analysis system for head-related product designs based on the Civilian American and European Surface Anthropometry Resource (CAESAR) database of North Americans. A total of 2299 heads in the CAESAR database were manually edited and 26 anthropometric landmarks were marked on the edited 3D heads to measure 30 anthropometric dimensions related to head-related product designs. The 3D anthropometric sizing analysis system (3D-ASAS) developed in the study provides analysis functions of a sizing system and representative face models by considering a target product, a target population, the number of size categories, and key anthropometric dimensions based on the CAESAR head measurements. Further research to reduce the efforts of manual editing and landmarking of 3D body scan data is discussed for efficient application of the 3D-ASAS to the design process of various wearable products.

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This study aimed to compare a three-dimensional (3D) semiautomatic measurement protocol (3D-SAMP) that measures hand dimensions using a plaster hand and a 3D scanner with the conventional direct measurement protocol (DMP). An experiment was conducted to measure 52 dimensions of one hand by 20 measurers with three repetitions. The locations of landmarks attached to the plaster hand were automatically identified and then measurements of the hand dimensions were automatically extracted in the 3D-SAMP. Significant measurement differences with a range of 2.1 to 4.4 mm between the 3D-SAMP and the DMP were observed in 13 out of the 52 dimensions, and the 3D-SAMP showed better reliability than the DMP in terms of intra- and intermeasurer variability. The 3D-SAMP was found significantly faster and easier in hand measurement than the DMP (11.1 ± 3.5 min for 3D-SAMP and 17.8 ± 4.5 min for DMP; 5.2 ± 0.8 for 3D-SAMP and 4.3 ± 0.8 for DMP using a 7-point scale with 1 for very dissatisfied and 7 for very satisfied for ease of measurement) when fabrication (about 1 hr 10 min) and scanning (3 min) of a plaster hand were not considered. The proposed 3D-SAMP is applicable only to plaster hands available in hand measurement.

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An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male
participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG
measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).@en