The present study aimed to explore the effects of motionless imagery training with an avatar in virtual reality(VR)on emotion,cognition,and physiological response changes in healthy adults.Participants were 30 healthy...The present study aimed to explore the effects of motionless imagery training with an avatar in virtual reality(VR)on emotion,cognition,and physiological response changes in healthy adults.Participants were 30 healthy adults aged between 19 and 35 years.All participants were randomly divided into the experimental group(n=18),which executed the imagery training with an avatar in VR,or the control group(n=12),which merely experienced the VR without an avatar.Both groups underwent the intervention,a 20-min session,3 times a week for 6 weeks.VR experience questionnaires and physiological response changes were measured at pre-and post-test and emotional states and cognition tests were measured at pre-,post-,and follow-up test.The experimental group showed no significant changes in the Presence Questionnaire(PQ)and the Simulator Sickness Questionnaire(SSQ)after the intervention while the control group showed a significant decrease in the PQ after the intervention.In all emotional states,there were no significant differences in the interaction between times and groups.A significant main effect of time was revealed in all cognition tests except the delayed recall and the delayed recognition in K-Auditory Verbal Learning Test(K-AVLT).In physiological response changes,the experimental group showed significant improvements in the electromyogram(EMG)at rectus femoris on the left side after the intervention.Thus,imagery training with an avatar in VR can be considered to be effective for enhancements of cognitions and physiological response changes.展开更多
Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless fore...Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.展开更多
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MIST,2017R1C1B5018351)by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MIST,NRF-2020R1F1A1072241).
文摘The present study aimed to explore the effects of motionless imagery training with an avatar in virtual reality(VR)on emotion,cognition,and physiological response changes in healthy adults.Participants were 30 healthy adults aged between 19 and 35 years.All participants were randomly divided into the experimental group(n=18),which executed the imagery training with an avatar in VR,or the control group(n=12),which merely experienced the VR without an avatar.Both groups underwent the intervention,a 20-min session,3 times a week for 6 weeks.VR experience questionnaires and physiological response changes were measured at pre-and post-test and emotional states and cognition tests were measured at pre-,post-,and follow-up test.The experimental group showed no significant changes in the Presence Questionnaire(PQ)and the Simulator Sickness Questionnaire(SSQ)after the intervention while the control group showed a significant decrease in the PQ after the intervention.In all emotional states,there were no significant differences in the interaction between times and groups.A significant main effect of time was revealed in all cognition tests except the delayed recall and the delayed recognition in K-Auditory Verbal Learning Test(K-AVLT).In physiological response changes,the experimental group showed significant improvements in the electromyogram(EMG)at rectus femoris on the left side after the intervention.Thus,imagery training with an avatar in VR can be considered to be effective for enhancements of cognitions and physiological response changes.
基金National Natural Science Foundations of China(Nos.61374097,61601108)the Fundamental Research Funds for the Central Universities,China(No.N130423006)the Foundation of Northeastern University at Qinhuangdao,China(No.XNK201403)
文摘Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.
