The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Tomoyuki Yambe - One of the best experts on this subject based on the ideXlab platform.
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evaluation of temporal relationship between a physiological index and a Subjective Score using average mutual information
Displays, 2011Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Noriyasu Homma, Shinichi NittaAbstract:Abstract Recently, because of the ubiquitous popularization of home video cameras, countless people have had opportunities to watch video images captured by amateur cameramen. Because of this, concerns have arisen over potential negative impacts on viewer health, such as visually-induced motion sickness (VIMS). To determine the mechanism inducing VIMS and to establish a method of preventing it, it is necessary to understand which types of video scenes are associated with the onset of VIMS. Furthermore, while it is useful to consider viewer self-assessments while watching such scenes, physiological indices can provide even more information because they can be measured second-by-second in real time. However, there is not much knowledge regarding the temporal relationships between the severity of VIMS and its accompanying physiological conditions. In this study, the average mutual information was employed to determine the temporal relationship between Subjective evaluation Scores (a subject’s personal evaluation of his/her own condition) and various physiological indices present when people suffer from VIMS. Our analysis of experimental data found that changes in the two physiological indices, which were respiratory sinus arrhythmia and the maximum cross-correlation coefficient between heart rate and pulse transmission time, had a concordance rate of more than 60% with changes in the severity of VIMS symptoms experienced by test subjects. Furthermore, we determined that it may be possible to detect signs of impending VIMS prior to the development of symptoms by analyzing physiological indices.
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Dynamic characteristics between the Subjective Score of motion sickness discomfort and video global motion
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:It is well-known that visually-induced motion sickness (VIMS) is caused by image motion. Therefore it is important to clarify the relationship between image motion and the change in discomfort level. However, it is difficult to know the quick change in the level of discomfort during watching actual video image. The authors have proposed a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. In this study, the model which represents the relation between global motion vectors of a video image and estimated Subjective Score was identified as ARX model. The results indicated that the simple ARX model can estimate the change in Subjective Score from global motion vectors.
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relationship between physiological indices and a Subjective Score in evaluating visually induced motion sickness
International Conference on Human-Computer Interaction, 2009Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual environments are evolving rapidly along with the popularization of high resolution and wide field-of-view displays. However, there is a concern that these environments may give negative effects on viewers' health such as visually-induced motion sickness (VIMS). Previous studies reported that some physiological indices were useful to assess the effect of visual stimulation. However, we have little knowledge about temporal relationship between the severity of sickness and the change in the physiological indices. In this study, the average mutual information has been employed to investigate this relationship. The analysis of experimental data has suggested that there is a possibility to detect a sign of VIMS prior to the development of symptoms of VIMS with the physiological indices.
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Interpolation of the Subjective Score of visually-induced motion sickness by using physiological parameters
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:In order to investigate the effect of visually-induced motion sickness in actual video images, this study proposes a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. The estimation results indicated that the model can represent, in higher resolution, the change in the Subjective Score of the subjects who have induced nausea.
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Evaluation of Adaptation to Visually Induced Motion Sickness by Using Physiological Index Associated with Baroreflex Function
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual images including intensive motions and the experience of virtual reality sometimes induce visually-indeuced motion sickness (VIMS). There are few studies that have objectively evaluated the effects of repetitive exposures to these stimuli on humans. In this study, an experiment was carried out in which the same video image was presented to human subjects three times. We evaluated changes of the intensity of VIMS they suffered from with a Subjective Score and a physiological index, rhomax which is defined as the maximum cross-correlation coefficient between heart rate and pulse wave transmission time and is considered to reflect baroreflex function. The results showed that the adaptation to VIMS could be represented by a decrease in the objective index rhomax as well as the Subjective Score. On the contrary, however, some subjects' rhomax increased in a few similar time regions at every trial. This fact suggests that we can specify the part of the video image which is closely related to VIMS by analyzing the change in rhomax with time.
Akira Tanaka - One of the best experts on this subject based on the ideXlab platform.
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evaluation of temporal relationship between a physiological index and a Subjective Score using average mutual information
Displays, 2011Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Noriyasu Homma, Shinichi NittaAbstract:Abstract Recently, because of the ubiquitous popularization of home video cameras, countless people have had opportunities to watch video images captured by amateur cameramen. Because of this, concerns have arisen over potential negative impacts on viewer health, such as visually-induced motion sickness (VIMS). To determine the mechanism inducing VIMS and to establish a method of preventing it, it is necessary to understand which types of video scenes are associated with the onset of VIMS. Furthermore, while it is useful to consider viewer self-assessments while watching such scenes, physiological indices can provide even more information because they can be measured second-by-second in real time. However, there is not much knowledge regarding the temporal relationships between the severity of VIMS and its accompanying physiological conditions. In this study, the average mutual information was employed to determine the temporal relationship between Subjective evaluation Scores (a subject’s personal evaluation of his/her own condition) and various physiological indices present when people suffer from VIMS. Our analysis of experimental data found that changes in the two physiological indices, which were respiratory sinus arrhythmia and the maximum cross-correlation coefficient between heart rate and pulse transmission time, had a concordance rate of more than 60% with changes in the severity of VIMS symptoms experienced by test subjects. Furthermore, we determined that it may be possible to detect signs of impending VIMS prior to the development of symptoms by analyzing physiological indices.
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Dynamic characteristics between the Subjective Score of motion sickness discomfort and video global motion
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:It is well-known that visually-induced motion sickness (VIMS) is caused by image motion. Therefore it is important to clarify the relationship between image motion and the change in discomfort level. However, it is difficult to know the quick change in the level of discomfort during watching actual video image. The authors have proposed a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. In this study, the model which represents the relation between global motion vectors of a video image and estimated Subjective Score was identified as ARX model. The results indicated that the simple ARX model can estimate the change in Subjective Score from global motion vectors.
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relationship between physiological indices and a Subjective Score in evaluating visually induced motion sickness
International Conference on Human-Computer Interaction, 2009Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual environments are evolving rapidly along with the popularization of high resolution and wide field-of-view displays. However, there is a concern that these environments may give negative effects on viewers' health such as visually-induced motion sickness (VIMS). Previous studies reported that some physiological indices were useful to assess the effect of visual stimulation. However, we have little knowledge about temporal relationship between the severity of sickness and the change in the physiological indices. In this study, the average mutual information has been employed to investigate this relationship. The analysis of experimental data has suggested that there is a possibility to detect a sign of VIMS prior to the development of symptoms of VIMS with the physiological indices.
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Interpolation of the Subjective Score of visually-induced motion sickness by using physiological parameters
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:In order to investigate the effect of visually-induced motion sickness in actual video images, this study proposes a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. The estimation results indicated that the model can represent, in higher resolution, the change in the Subjective Score of the subjects who have induced nausea.
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Evaluation of Adaptation to Visually Induced Motion Sickness by Using Physiological Index Associated with Baroreflex Function
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual images including intensive motions and the experience of virtual reality sometimes induce visually-indeuced motion sickness (VIMS). There are few studies that have objectively evaluated the effects of repetitive exposures to these stimuli on humans. In this study, an experiment was carried out in which the same video image was presented to human subjects three times. We evaluated changes of the intensity of VIMS they suffered from with a Subjective Score and a physiological index, rhomax which is defined as the maximum cross-correlation coefficient between heart rate and pulse wave transmission time and is considered to reflect baroreflex function. The results showed that the adaptation to VIMS could be represented by a decrease in the objective index rhomax as well as the Subjective Score. On the contrary, however, some subjects' rhomax increased in a few similar time regions at every trial. This fact suggests that we can specify the part of the video image which is closely related to VIMS by analyzing the change in rhomax with time.
Norihiro Sugita - One of the best experts on this subject based on the ideXlab platform.
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evaluation of temporal relationship between a physiological index and a Subjective Score using average mutual information
Displays, 2011Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Noriyasu Homma, Shinichi NittaAbstract:Abstract Recently, because of the ubiquitous popularization of home video cameras, countless people have had opportunities to watch video images captured by amateur cameramen. Because of this, concerns have arisen over potential negative impacts on viewer health, such as visually-induced motion sickness (VIMS). To determine the mechanism inducing VIMS and to establish a method of preventing it, it is necessary to understand which types of video scenes are associated with the onset of VIMS. Furthermore, while it is useful to consider viewer self-assessments while watching such scenes, physiological indices can provide even more information because they can be measured second-by-second in real time. However, there is not much knowledge regarding the temporal relationships between the severity of VIMS and its accompanying physiological conditions. In this study, the average mutual information was employed to determine the temporal relationship between Subjective evaluation Scores (a subject’s personal evaluation of his/her own condition) and various physiological indices present when people suffer from VIMS. Our analysis of experimental data found that changes in the two physiological indices, which were respiratory sinus arrhythmia and the maximum cross-correlation coefficient between heart rate and pulse transmission time, had a concordance rate of more than 60% with changes in the severity of VIMS symptoms experienced by test subjects. Furthermore, we determined that it may be possible to detect signs of impending VIMS prior to the development of symptoms by analyzing physiological indices.
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Dynamic characteristics between the Subjective Score of motion sickness discomfort and video global motion
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:It is well-known that visually-induced motion sickness (VIMS) is caused by image motion. Therefore it is important to clarify the relationship between image motion and the change in discomfort level. However, it is difficult to know the quick change in the level of discomfort during watching actual video image. The authors have proposed a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. In this study, the model which represents the relation between global motion vectors of a video image and estimated Subjective Score was identified as ARX model. The results indicated that the simple ARX model can estimate the change in Subjective Score from global motion vectors.
-
relationship between physiological indices and a Subjective Score in evaluating visually induced motion sickness
International Conference on Human-Computer Interaction, 2009Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual environments are evolving rapidly along with the popularization of high resolution and wide field-of-view displays. However, there is a concern that these environments may give negative effects on viewers' health such as visually-induced motion sickness (VIMS). Previous studies reported that some physiological indices were useful to assess the effect of visual stimulation. However, we have little knowledge about temporal relationship between the severity of sickness and the change in the physiological indices. In this study, the average mutual information has been employed to investigate this relationship. The analysis of experimental data has suggested that there is a possibility to detect a sign of VIMS prior to the development of symptoms of VIMS with the physiological indices.
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Interpolation of the Subjective Score of visually-induced motion sickness by using physiological parameters
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:In order to investigate the effect of visually-induced motion sickness in actual video images, this study proposes a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. The estimation results indicated that the model can represent, in higher resolution, the change in the Subjective Score of the subjects who have induced nausea.
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Evaluation of Adaptation to Visually Induced Motion Sickness by Using Physiological Index Associated with Baroreflex Function
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual images including intensive motions and the experience of virtual reality sometimes induce visually-indeuced motion sickness (VIMS). There are few studies that have objectively evaluated the effects of repetitive exposures to these stimuli on humans. In this study, an experiment was carried out in which the same video image was presented to human subjects three times. We evaluated changes of the intensity of VIMS they suffered from with a Subjective Score and a physiological index, rhomax which is defined as the maximum cross-correlation coefficient between heart rate and pulse wave transmission time and is considered to reflect baroreflex function. The results showed that the adaptation to VIMS could be represented by a decrease in the objective index rhomax as well as the Subjective Score. On the contrary, however, some subjects' rhomax increased in a few similar time regions at every trial. This fact suggests that we can specify the part of the video image which is closely related to VIMS by analyzing the change in rhomax with time.
Makoto Yoshizawa - One of the best experts on this subject based on the ideXlab platform.
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evaluation of temporal relationship between a physiological index and a Subjective Score using average mutual information
Displays, 2011Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Noriyasu Homma, Shinichi NittaAbstract:Abstract Recently, because of the ubiquitous popularization of home video cameras, countless people have had opportunities to watch video images captured by amateur cameramen. Because of this, concerns have arisen over potential negative impacts on viewer health, such as visually-induced motion sickness (VIMS). To determine the mechanism inducing VIMS and to establish a method of preventing it, it is necessary to understand which types of video scenes are associated with the onset of VIMS. Furthermore, while it is useful to consider viewer self-assessments while watching such scenes, physiological indices can provide even more information because they can be measured second-by-second in real time. However, there is not much knowledge regarding the temporal relationships between the severity of VIMS and its accompanying physiological conditions. In this study, the average mutual information was employed to determine the temporal relationship between Subjective evaluation Scores (a subject’s personal evaluation of his/her own condition) and various physiological indices present when people suffer from VIMS. Our analysis of experimental data found that changes in the two physiological indices, which were respiratory sinus arrhythmia and the maximum cross-correlation coefficient between heart rate and pulse transmission time, had a concordance rate of more than 60% with changes in the severity of VIMS symptoms experienced by test subjects. Furthermore, we determined that it may be possible to detect signs of impending VIMS prior to the development of symptoms by analyzing physiological indices.
-
Dynamic characteristics between the Subjective Score of motion sickness discomfort and video global motion
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:It is well-known that visually-induced motion sickness (VIMS) is caused by image motion. Therefore it is important to clarify the relationship between image motion and the change in discomfort level. However, it is difficult to know the quick change in the level of discomfort during watching actual video image. The authors have proposed a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. In this study, the model which represents the relation between global motion vectors of a video image and estimated Subjective Score was identified as ARX model. The results indicated that the simple ARX model can estimate the change in Subjective Score from global motion vectors.
-
relationship between physiological indices and a Subjective Score in evaluating visually induced motion sickness
International Conference on Human-Computer Interaction, 2009Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual environments are evolving rapidly along with the popularization of high resolution and wide field-of-view displays. However, there is a concern that these environments may give negative effects on viewers' health such as visually-induced motion sickness (VIMS). Previous studies reported that some physiological indices were useful to assess the effect of visual stimulation. However, we have little knowledge about temporal relationship between the severity of sickness and the change in the physiological indices. In this study, the average mutual information has been employed to investigate this relationship. The analysis of experimental data has suggested that there is a possibility to detect a sign of VIMS prior to the development of symptoms of VIMS with the physiological indices.
-
Interpolation of the Subjective Score of visually-induced motion sickness by using physiological parameters
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Akira Tanaka, Norihiro Sugita, Makoto Yoshizawa, Tomoyuki YambeAbstract:In order to investigate the effect of visually-induced motion sickness in actual video images, this study proposes a method of interpolation for the Subjective Score, which has low time and quantitative resolutions, by using physiological parameters. The model which represents the change in Subjective Score of VIMS was expressed as multiple regression equations in which input parameters are cardiovascular indices such as heart rate variability. The estimation results indicated that the model can represent, in higher resolution, the change in the Subjective Score of the subjects who have induced nausea.
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Evaluation of Adaptation to Visually Induced Motion Sickness by Using Physiological Index Associated with Baroreflex Function
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Norihiro Sugita, Akira Tanaka, Makoto Yoshizawa, Tomoyuki Yambe, Shigeru Chiba, Shinichi NittaAbstract:Visual images including intensive motions and the experience of virtual reality sometimes induce visually-indeuced motion sickness (VIMS). There are few studies that have objectively evaluated the effects of repetitive exposures to these stimuli on humans. In this study, an experiment was carried out in which the same video image was presented to human subjects three times. We evaluated changes of the intensity of VIMS they suffered from with a Subjective Score and a physiological index, rhomax which is defined as the maximum cross-correlation coefficient between heart rate and pulse wave transmission time and is considered to reflect baroreflex function. The results showed that the adaptation to VIMS could be represented by a decrease in the objective index rhomax as well as the Subjective Score. On the contrary, however, some subjects' rhomax increased in a few similar time regions at every trial. This fact suggests that we can specify the part of the video image which is closely related to VIMS by analyzing the change in rhomax with time.
Yong Man Ro - One of the best experts on this subject based on the ideXlab platform.
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Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Yong Man RoAbstract:In this paper, we propose a novel deep learning-based virtual reality image quality assessment method that automatically predicts the visual quality of an omnidirectional image. In order to assess the visual quality in viewing the omnidirectional image, we propose deep networks consisting of virtual reality (VR) quality Score predictor and human perception guider. The proposed VR quality Score predictor learns the positional and visual characteristics of the omnidirectional image by encoding the positional feature and visual feature of a patch on the omnidirectional image. With the encoded positional feature and visual feature, patch weight and patch quality Score are estimated. Then, by aggregating all weights and Scores of the patches, the image quality Score is predicted. The proposed human perception guider evaluates the predicted quality Score by referring to the human Subjective Score (i.e., ground-truth obtained by subjects) using an adversarial learning. With adversarial learning, the VR quality Score predictor is trained to accurately predict the quality Score in order to deceive the guider, while the proposed human perception guider is trained to precisely distinguish between the predictor Score and the ground-truth Subjective Score. To verify the performance of the proposed method, we conducted comprehensive Subjective experiments and evaluated the performance of the proposed method. The experimental results show that the proposed method outperforms the existing two-dimentional image quality models and the state-of-the-art image quality models for omnidirectional images.
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Binocular Fusion Net: Deep Learning Visual Comfort Assessment for Stereoscopic 3D
IEEE Transactions on Circuits and Systems for Video Technology, 2019Co-Authors: Hyunwook Jeong, Yong Man RoAbstract:In this paper, we propose a novel deep learning-based visual comfort assessment (VCA) for stereoscopic images. To assess the overall degree of visual discomfort in stereoscopic viewing, we devise a binocular fusion deep network (BFN) learning binocular characteristics between stereoscopic images. The proposed BFN learns the latent binocular feature representations for the visual comfort Score prediction. In the BFN, the binocular feature is encoded by fusing the spatial features extracted from left and right views. Finally, the visual comfort Score is predicted by projecting the binocular feature onto the Subjective Score space. In addition, we devise a disparity regularization network (DRN) for improving the prediction results. The proposed DRN takes the binocular feature from the BFN and estimates disparity maps from the feature in order to embed disparity relations between left and right views into the deep network. The proposed deep network with BFN and DRN is end-to-end trained in a unified framework in which the DRN acts as disparity regularization. We evaluated the prediction performance of the proposed deep network for VCA by the comparison of existing objective VCA metrics. Further, we demonstrated that the proposed BFN showed various factors causing visual discomfort by using network visualization.
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VRSA Net: VR Sickness Assessment Considering Exceptional Motion for 360° VR Video
IEEE Transactions on Image Processing, 2019Co-Authors: Yong Man RoAbstract:The viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness could occur when watching immersive VR content. To deal with the viewing safety for VR content, objective assessment of VR sickness is of great importance. In this paper, we propose a novel objective VR sickness assessment (VRSA) network based on deep generative model for automatically predicting the VR sickness Score. The proposed method takes into account motion patterns of VR videos in which an exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, which are VR video generator and VR sickness Score predictor. By training the VR video generator with common videos with non-exceptional motion, the generator learns the tolerance of VR sickness in human motion perception. As a result, the difference between the original and the generated videos by the VR video generator could represent exceptional motion of VR video causing VR sickness. In the VR sickness Score predictor, the VR sickness Score is predicted by projecting the difference between the original and the generated videos onto the Subjective Score space. For the evaluation of VR sickness assessment, we built a new dataset which consists of 360° videos (stimuli), corresponding physiological signals, and Subjective questionnaires from Subjective assessment experiments. Experimental results demonstrated that the proposed VRSA network achieved a high correlation with human perceptual Score for VR sickness.
