Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clini...Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clinical diagnosis and assessment.In this study,we propose an innovative seizure detection method based on video feature recognition of patient spasms.To capture the temporal characteristics of the spasm behavior presented in the videos effectively,we incorporate asymmetric convolutions and convolution–batch normalization–ReLU(CBR)modules.Specifically within the 3D-ResNet residual blocks,we split the larger convolutional kernels into two asymmetric 3D convolutional kernels.These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features,both horizontally and vertically.In addition,we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently.To improve the generalization ability,we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements.We train and evaluate our method using the PLA IESS-VIDEO dataset,achieving an average seizure recognition accuracy of 90.59%,precision of 90.94%,and recall of 87.64%.To validate its generalization capability further,we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers.The final test results demonstrate that our method achieved a recall of 0.6476,surpassing the average level achieved by human experts(0.5595),while attaining a high F1-score of 0.7219.These findings have substantial significance for the long-term assessment of patients with IESS.展开更多
Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent adva...Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the field.In order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function design.Firstly,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these methods.Furthermore,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation.Lastly,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID.This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain.展开更多
Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods ca...Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.展开更多
A new video-based measurement is proposed to collect and investigate traffic flow parameters. The output of the measurement is velocity-headway distance data pairs. Because density can be directly acquired by the reci...A new video-based measurement is proposed to collect and investigate traffic flow parameters. The output of the measurement is velocity-headway distance data pairs. Because density can be directly acquired by the reciprocal of headway distance, the data pairs have the advantage of better simultaneity than those from common detectors. By now, over 33 000 pairs of data have been collected from two road sections in the cities of Shanghai and Zhengzhou. Through analyzing the video files recording traffic movements on urban expressways, the following issues are studied：laws of vehicle velocity changing with headway distance, proportions of di0erent driving behaviors in the traffic system, and characteristics of traffic flow in snowy days. The results show that the real road traffic is very complex, and factors such as location and climate need to be taken into consideration in the formation of traffic flow models.展开更多
Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overal...Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life.This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions(VBIs)for those with ASD during the critical years of adolescence and young adulthood.The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational,daily living,and academic skills.In addition,VBIs allow for the maintenance and generalization of the different target behaviors that were examined.The majority of the studies located by this review also investigated the social validity of the intervention method with participants and caregivers and found these VBIs to have high social validity.Although a few studies that implemented VBIs to improve academic skills were located,the research on their use in this area was found to be lacking,indicating a gap in the research on VBIs.Increased usage of VBIs—including video modeling and video prompting—with the target population of those aged 15–28 with ASD is recommended with specific attention given to the use of VBIs to improve the academic and social skills of adolescents and young adults with ASD.展开更多
This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, i...This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.展开更多
In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mod...In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.展开更多
In recent years,gesture recognition has been widely used in the fields of intelligent driving,virtual reality,and human-computer interaction.With the development of artificial intelligence,deep learning has achieved r...In recent years,gesture recognition has been widely used in the fields of intelligent driving,virtual reality,and human-computer interaction.With the development of artificial intelligence,deep learning has achieved remarkable success in computer vision.To help researchers better understanding the development status of gesture recognition in video,this article provides a detailed survey of the latest developments in gesture recognition technology for videos based on deep learning.The reviewed methods are broadly categorized into three groups based on the type of neural networks used for recognition:two stream convolutional neural networks,3D convolutional neural networks,and Long-short Term Memory(LSTM)networks.In this review,we discuss the advantages and limitations of existing technologies,focusing on the feature extraction method of the spatiotemporal structure information in a video sequence,and consider future research directions.展开更多
Purpose:This study aimed to examine the usage,acceptability,usability,perceived usefulness,and satisfaction of a web-based video-tailored physical activity(PA)intervention(TaylorActive)in adults.Methods:In 2013-2014,5...Purpose:This study aimed to examine the usage,acceptability,usability,perceived usefulness,and satisfaction of a web-based video-tailored physical activity(PA)intervention(TaylorActive)in adults.Methods:In 2013-2014,501 Australian adults aged 18+years were randomized into a video-tailored intervention,text-tailored intervention,or control group.Over 3 months,the intervention groups received access to 8 sessions of personally tailored PA advice delivered via the TaylorActive website.Only the delivery method differed between the intervention groups:video-tailored vs.text-tailored.Google Analytics and telephone surveys conducted at post intervention(3 months)were used to assess intervention usage,acceptability,usability,perceived usefulness,and satisfaction.Quantitative and qualitative process data were analyzed using descriptive statistics and thematic content analysis.Results:Of 501 recruited adults,259 completed the 3-month post-intervention survey(52%retention).Overall,usage of the TaylorActive website with respect to number of website visits,intervention sessions,and action plans completed was modest in both the video-tailored(7.6士7.2 visits,mean±SD)and text-tailored(7.3±5.4 visits)groups with no significant between-group differences.The majority of participants in all groups used the TaylorActive website less than once in 2 weeks(66.7%video-tailored,62.7%text-tailored,87.5%control;p<0.001).Acceptability was rated mostly high in all groups and in some instances,significantly higher in the intervention groups compared to the control group(p<0.010).Usability was also rated high;mean Systems Usability Scores were 77.3(video-tailored),75.7(text-tailored),and 74.1(control)with no significant between-group differences.Perceived usefulness of the TaylorActive intervention was low,though mostly rated higher in the intervention groups compared to the control group(p<0.010).Satisfaction with the TaylorActive website was mixed.Participants in both intervention groups liked its ease of use,personalized feedback,and tracking of progress,but also found completing action plans and survey questions for each session repetitive and tedious.Conclusion:Providing personally tailored PA advice on its own(through either video or text)is likely insufficient to ensure good retention,usage,perceived usefulness,and satisfaction with a web-based PA intervention.Strategies to address this may include the incorporation of additional intervention components such as activity trackers,social interactions,gamification,as well as the use of advanced artificial intelligence and machine learning technologies to allow more personalized dialogue with participants.展开更多
In recent years, many image-based rendering techniques have advanced from static to dynamic scenes and thus become video-based rendering （VBR） methods. But actually, only a few of them can render new views on-line. ...In recent years, many image-based rendering techniques have advanced from static to dynamic scenes and thus become video-based rendering （VBR） methods. But actually, only a few of them can render new views on-line. We present a new VBR system that creates new views of a live dynamic scene. This system provides high quality images and does not require any background subtraction. Our method follows a plane-sweep approach and reaches real-time rendering using consumer graphic hardware, graphics processing unit （GPU）. Only one computer is used for both acquisition and rendering. The video stream acquisition is performed by at least 3 webcams. We propose an additional video stream management that extends the number of webcams to 10 or more. These considerations make our system low-cost and hence accessible for everyone. We also present an adaptation of our plane-sweep method to create simultaneously multiple views of the scene in real-time. Our system is especially designed for stereovision using autostereoscopic displays. The new views are computed from 4 webcams connected to a computer and are compressed in order to be transfered to a mobile phone. Using GPU programming, our method provides up to 16 images of the scene in real-time. The use of both GPU and CPU makes this method work on only one consumer grade computer.展开更多
Background:With more than two billion people infected worldwide,soil-transmitted helminths(STH)are the most widespread infections.To date,STH control efforts rely predominantly on recurrent mass drug administration(MD...Background:With more than two billion people infected worldwide,soil-transmitted helminths(STH)are the most widespread infections.To date,STH control efforts rely predominantly on recurrent mass drug administration(MDA),which does not prevent reinfection.Additional public health measures including novel health educational tools are required for more sustained integrated control of STH.We describe the development of an educational cartoon video(The Magic Glasses)targeting STH infections in Chinese schoolchildren and its pilot testing in China.We applied an extensive community-based mixed methods approach involving input from the target group of 9–10 year old schoolchildren and key informants,such as teachers,doctors and parents,in order to identify potential STH infection risks in the study area and to formulate key messages for the cartoon.The development of the educational cartoon included three major steps:formative research,production,and pilot testing and revision.Results:We found that most adults and approximately 50%of the schoolchildren were aware of roundworm(Ascaris)infection,but knowledge of transmission,prevention and treatment of STH was poor.Observations in the study area showed that unhygienic food practices,such as eating raw and unwashed fruit or playing in vegetable gardens previously fertilised with human faeces,posed major STH infection risks.Conclusions:It was crucial to assess the intellectual,emotional,social and cultural background of the target population prior to video production in order to integrate the key messages of the cartoon into everyday situations.Overall,our strategy for the development of the cartoon and its incorporation into a health education package proved successful,and we provide a summary of recommendations for the development of future educational videos based on our experiences in China.展开更多
基金the National Social Science Foundation of China(No.21BTQ106),the Natural Science Foundation of Beijing(No.7222187),and the Key Project of Innovation Cultivation Fund of the Seventh Medical Center of PLA General Hospital(No.qzx-2023-1)。
文摘Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clinical diagnosis and assessment.In this study,we propose an innovative seizure detection method based on video feature recognition of patient spasms.To capture the temporal characteristics of the spasm behavior presented in the videos effectively,we incorporate asymmetric convolutions and convolution–batch normalization–ReLU(CBR)modules.Specifically within the 3D-ResNet residual blocks,we split the larger convolutional kernels into two asymmetric 3D convolutional kernels.These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features,both horizontally and vertically.In addition,we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently.To improve the generalization ability,we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements.We train and evaluate our method using the PLA IESS-VIDEO dataset,achieving an average seizure recognition accuracy of 90.59%,precision of 90.94%,and recall of 87.64%.To validate its generalization capability further,we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers.The final test results demonstrate that our method achieved a recall of 0.6476,surpassing the average level achieved by human experts(0.5595),while attaining a high F1-score of 0.7219.These findings have substantial significance for the long-term assessment of patients with IESS.
基金We acknowledge funding from National Natural Science Foundation of China under Grants Nos.62101213,62103165the Shandong Provincial Natural Science Foundation under Grant Nos.ZR2020QF107,ZR2020MF137,ZR2021QF043.
文摘Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the field.In order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function design.Firstly,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these methods.Furthermore,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation.Lastly,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID.This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain.
基金supported by the National Key Research and Development Program of China(2017YFA0105203,2017YFA0105201)National Science Foundation of China(31771076,81925011)+2 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(CAS)(XDB32040201)Beijing Academy of Artificial IntelligenceKey-Area Research and Development Program of Guangdong Province(2019B030335001)。
文摘Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.
基金supported by the National Natural Science Foundation of China (10772050)
文摘A new video-based measurement is proposed to collect and investigate traffic flow parameters. The output of the measurement is velocity-headway distance data pairs. Because density can be directly acquired by the reciprocal of headway distance, the data pairs have the advantage of better simultaneity than those from common detectors. By now, over 33 000 pairs of data have been collected from two road sections in the cities of Shanghai and Zhengzhou. Through analyzing the video files recording traffic movements on urban expressways, the following issues are studied：laws of vehicle velocity changing with headway distance, proportions of di0erent driving behaviors in the traffic system, and characteristics of traffic flow in snowy days. The results show that the real road traffic is very complex, and factors such as location and climate need to be taken into consideration in the formation of traffic flow models.
文摘Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life.This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions(VBIs)for those with ASD during the critical years of adolescence and young adulthood.The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational,daily living,and academic skills.In addition,VBIs allow for the maintenance and generalization of the different target behaviors that were examined.The majority of the studies located by this review also investigated the social validity of the intervention method with participants and caregivers and found these VBIs to have high social validity.Although a few studies that implemented VBIs to improve academic skills were located,the research on their use in this area was found to be lacking,indicating a gap in the research on VBIs.Increased usage of VBIs—including video modeling and video prompting—with the target population of those aged 15–28 with ASD is recommended with specific attention given to the use of VBIs to improve the academic and social skills of adolescents and young adults with ASD.
文摘This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.
基金supported by the National Natural Science Foundation of China(No.62001209).
文摘In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.
基金the National Key R&D Program of China(2018YFC0807500)the National Natural Science Foundation of China(61772396,61772392,62002271,61902296)+3 种基金the Fundamental Research Funds for the Central Universities(JBF180301,XJS210310,XJS190307)Xi'an Key Laboratory of Big Data and Intelligent Vision(201805053ZD4CG37)the National Natural Science Foundation of Shaanxi Province(2020JQ-330,2020JM-195)the China Postdoctoral Science Foundation(2019M663640).
文摘In recent years,gesture recognition has been widely used in the fields of intelligent driving,virtual reality,and human-computer interaction.With the development of artificial intelligence,deep learning has achieved remarkable success in computer vision.To help researchers better understanding the development status of gesture recognition in video,this article provides a detailed survey of the latest developments in gesture recognition technology for videos based on deep learning.The reviewed methods are broadly categorized into three groups based on the type of neural networks used for recognition:two stream convolutional neural networks,3D convolutional neural networks,and Long-short Term Memory(LSTM)networks.In this review,we discuss the advantages and limitations of existing technologies,focusing on the feature extraction method of the spatiotemporal structure information in a video sequence,and consider future research directions.
基金funded by the National Health and Medical Research Council(1049369).CV(100427),MJD(100029),and SS(101240)were,and SA(102609)is currentlysupported by a research fellowship from the National Heart Foundation of Australia.CES(1090517),RCP(1100138),and AR(1105926)were,and MJD(1141606)and SS(1125586)are currentlysupported by a research fellowship from the National Health and Medical Research Council。
文摘Purpose:This study aimed to examine the usage,acceptability,usability,perceived usefulness,and satisfaction of a web-based video-tailored physical activity(PA)intervention(TaylorActive)in adults.Methods:In 2013-2014,501 Australian adults aged 18+years were randomized into a video-tailored intervention,text-tailored intervention,or control group.Over 3 months,the intervention groups received access to 8 sessions of personally tailored PA advice delivered via the TaylorActive website.Only the delivery method differed between the intervention groups:video-tailored vs.text-tailored.Google Analytics and telephone surveys conducted at post intervention(3 months)were used to assess intervention usage,acceptability,usability,perceived usefulness,and satisfaction.Quantitative and qualitative process data were analyzed using descriptive statistics and thematic content analysis.Results:Of 501 recruited adults,259 completed the 3-month post-intervention survey(52%retention).Overall,usage of the TaylorActive website with respect to number of website visits,intervention sessions,and action plans completed was modest in both the video-tailored(7.6士7.2 visits,mean±SD)and text-tailored(7.3±5.4 visits)groups with no significant between-group differences.The majority of participants in all groups used the TaylorActive website less than once in 2 weeks(66.7%video-tailored,62.7%text-tailored,87.5%control;p<0.001).Acceptability was rated mostly high in all groups and in some instances,significantly higher in the intervention groups compared to the control group(p<0.010).Usability was also rated high;mean Systems Usability Scores were 77.3(video-tailored),75.7(text-tailored),and 74.1(control)with no significant between-group differences.Perceived usefulness of the TaylorActive intervention was low,though mostly rated higher in the intervention groups compared to the control group(p<0.010).Satisfaction with the TaylorActive website was mixed.Participants in both intervention groups liked its ease of use,personalized feedback,and tracking of progress,but also found completing action plans and survey questions for each session repetitive and tedious.Conclusion:Providing personally tailored PA advice on its own(through either video or text)is likely insufficient to ensure good retention,usage,perceived usefulness,and satisfaction with a web-based PA intervention.Strategies to address this may include the incorporation of additional intervention components such as activity trackers,social interactions,gamification,as well as the use of advanced artificial intelligence and machine learning technologies to allow more personalized dialogue with participants.
基金This work was supported by Foundation of Technology Supporting the Creation of Digital Media Contents project (CREST, JST), Japan
文摘In recent years, many image-based rendering techniques have advanced from static to dynamic scenes and thus become video-based rendering （VBR） methods. But actually, only a few of them can render new views on-line. We present a new VBR system that creates new views of a live dynamic scene. This system provides high quality images and does not require any background subtraction. Our method follows a plane-sweep approach and reaches real-time rendering using consumer graphic hardware, graphics processing unit （GPU）. Only one computer is used for both acquisition and rendering. The video stream acquisition is performed by at least 3 webcams. We propose an additional video stream management that extends the number of webcams to 10 or more. These considerations make our system low-cost and hence accessible for everyone. We also present an adaptation of our plane-sweep method to create simultaneously multiple views of the scene in real-time. Our system is especially designed for stereovision using autostereoscopic displays. The new views are computed from 4 webcams connected to a computer and are compressed in order to be transfered to a mobile phone. Using GPU programming, our method provides up to 16 images of the scene in real-time. The use of both GPU and CPU makes this method work on only one consumer grade computer.
文摘Background:With more than two billion people infected worldwide,soil-transmitted helminths(STH)are the most widespread infections.To date,STH control efforts rely predominantly on recurrent mass drug administration(MDA),which does not prevent reinfection.Additional public health measures including novel health educational tools are required for more sustained integrated control of STH.We describe the development of an educational cartoon video(The Magic Glasses)targeting STH infections in Chinese schoolchildren and its pilot testing in China.We applied an extensive community-based mixed methods approach involving input from the target group of 9–10 year old schoolchildren and key informants,such as teachers,doctors and parents,in order to identify potential STH infection risks in the study area and to formulate key messages for the cartoon.The development of the educational cartoon included three major steps:formative research,production,and pilot testing and revision.Results:We found that most adults and approximately 50%of the schoolchildren were aware of roundworm(Ascaris)infection,but knowledge of transmission,prevention and treatment of STH was poor.Observations in the study area showed that unhygienic food practices,such as eating raw and unwashed fruit or playing in vegetable gardens previously fertilised with human faeces,posed major STH infection risks.Conclusions:It was crucial to assess the intellectual,emotional,social and cultural background of the target population prior to video production in order to integrate the key messages of the cartoon into everyday situations.Overall,our strategy for the development of the cartoon and its incorporation into a health education package proved successful,and we provide a summary of recommendations for the development of future educational videos based on our experiences in China.
