International Journal of Affective Engineering
Online ISSN : 2187-5413
ISSN-L : 2187-5413
Advance online publication
Displaying 1-5 of 5 articles from this issue
  • Yutaka AOYAMA, Hisaya TANAKA
    Article ID: IJAE-D-23-00036
    Published: 2024
    Advance online publication: March 19, 2024
    JOURNAL OPEN ACCESS ADVANCE PUBLICATION

    It is useful to extract the time factor of gait from captured gait videos. However, this requires frame-by-frame counting, which is expensive in terms of manpower and time. In this study, we developed discrimination models to discriminate between the stance phase and the swing phase of a walker from a walking video. Using MediaPipe, a marker-less motion capture system that can be used with a single camera, we discriminated the stance phase and swing phases of a walker from the angular changes of the waist, knee, and ankle on each side of the walker in each frame. The results showed that the right leg and left leg were discriminated with 95.1% and 95.0% accuracy, respectively. The gait cycle was calculated from the discrimination results, and the average deviation was only 7.4% for the right leg and 4.2% for the left leg.

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  • Ryohei P. HASEGAWA, Shinya WATANABE, Akiko ISHII, Hideo TSURUSHIMA
    Article ID: IJAE-D-23-00042
    Published: 2024
    Advance online publication: March 19, 2024
    JOURNAL OPEN ACCESS ADVANCE PUBLICATION

    We have been developing a cognitive, brain-machine interface (BMI)-based, training system called the “Neurotrainer.” This system was initially designed to detect averaged event-related potentials (ERPs), which reflect momentary heightened attention, allowing these individuals to participate in cognitive training without the need for hands-on interaction. In this study, we expanded our method to decode single-trial ERPs during a racing game, with the expectation that neurofeedback would accelerate the training process. We assessed the performance of the prototype system with healthy volunteers. The decoding accuracy of the target character was approximately 54% for a single trial and 83% for five trials (chance level = 12.5%). Moreover, ERP responses were stronger in the feedback condition than in the no-feedback condition. These results suggest that the BMI could be an effective tool for cognitive training, as real-time neurofeedback influences the brain activation of the players.

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  • Yumeka OGURA, Shogo OKAMOTO, Yuki KOSUGE
    Article ID: IJAE-D-23-00015
    Published: 2024
    Advance online publication: February 14, 2024
    JOURNAL OPEN ACCESS ADVANCE PUBLICATION

    Psychophysical and affective experiences while watching audiovisual content can be antecedents to determine the overall evaluation of viewing experiences. However, the layered relationships among these experiences have been hardly discussed. We investigated these experiences and their semantic structure while watching hug scenes. We selected 38 adjectives describing hugs and classified them into three layers: psychophysical, affective, and overall evaluation. Participants scored each of the 24 videos containing hugging scenes using these adjectives. The structure among the three layers was computed: the four psychophysical factors affected the four affective factors, which in turn affected the two overall evaluation factors, i.e., joyful and reassuring. The model was confirmed to have statistical validity by structural equation modeling and semantic validity by experiments using dummy links. The results will lead to the determination of measures to enhance affective experiences when viewing videos, and the formulation of criteria for measuring and evaluating affective experiences.

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  • E. A. Chayani DILRUKSHI, Kasumi SATO, Mami ISHIKAWA, Yuta NISHIYAMA, S ...
    Article ID: IJAE-D-23-00017
    Published: 2024
    Advance online publication: February 14, 2024
    JOURNAL OPEN ACCESS ADVANCE PUBLICATION

    Mouthwashes provide an important means for improving oral hygiene. This study investigated the psychophysiological effects of mouthwashes with different binders during the recovery after a short-term stressor. In a within-subject experimental design, 21 healthy adults used four mouthwashes; MW A (no binder), MW B (Sodium alginate 0.16%), MW C (Sodium alginate 0.32%), and MW D (Xanthan gum 0.08%), after a 20-minute calculation task. Subjective evaluations and physiological responses including skin conductance level and electrocardiogram were recorded throughout the experiment. MW C exhibited an increased high-frequency (HF) component of heart rate variability (HRV) during recovery, and a significant difference in “strength of thickness” compared to other mouthwashes. MW D showed a greater decrease in heart rate and increased HF component of HRV compared to MW B. The findings suggest that mouthwashes with higher thickness levels effectively promote recovery from the physiological stress response, and are potentially suitable for relaxation purposes.

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  • Ryo HARADA, KyoungOk KIM, Masayuki TAKATERA
    Article ID: IJAE-D-23-00011
    Published: 2023
    Advance online publication: January 31, 2024
    JOURNAL OPEN ACCESS ADVANCE PUBLICATION

    To estimate the garment impression, we verified three regression models using design parameters, geometrical and texture features of images, and convolutional features of images. Adopting three-dimensional apparel simulation, we generated 375 images of a men's outdoor jacket by changing design parameters, namely the length, waist, hem circumference, and sleeve circumference. Nine participants evaluated the cool-uncool (kakkoī-kakkowarui in Japanese) impression of the garment images using a semantic differential method. We used the measured widths, angles, and G-type Fourier descriptors of the jacket images as geometrical features. For texture features, the fractal dimension and Haralick texture features (Asm, Entropy, Contrast, Correlation) of the images were used. Convolutional features were obtained from the images using a convolutional neural network. By using the design parameters and the obtained features, we estimated the image impression using three regression models: multiple linear regression, neural network, and light gradient boosting machine (LightGBM) models. As a result, the LightGBM with the design parameters had the highest mean correlation coefficient for all participants. The inclusion of design parameters was thus found to be effective in estimating the impression based on garment design parameters with LightGBM.

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