Chris Baber, A. Khattab, J. Hermsdörfer, A. Wing, M. Russell
We explore the ways in which smart objects can be used to cue actions as part of coaching for Activities of Daily Living (ADL) following brain damage or injury, such as might arise following a stroke. In this case, appropriate actions are cued for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. This requires objects to be instrumented so that they can recognize the actions that users perform. In order to provide appropriate cues, the objects also need to be able to display information to users, e.g., by changing their physical appearance or by providing auditory output. We discuss the ways in which information can be displayed to cue user action.
{"title":"Coaching through smart objects","authors":"Chris Baber, A. Khattab, J. Hermsdörfer, A. Wing, M. Russell","doi":"10.1145/3154862.3154938","DOIUrl":"https://doi.org/10.1145/3154862.3154938","url":null,"abstract":"We explore the ways in which smart objects can be used to cue actions as part of coaching for Activities of Daily Living (ADL) following brain damage or injury, such as might arise following a stroke. In this case, appropriate actions are cued for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. This requires objects to be instrumented so that they can recognize the actions that users perform. In order to provide appropriate cues, the objects also need to be able to display information to users, e.g., by changing their physical appearance or by providing auditory output. We discuss the ways in which information can be displayed to cue user action.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wellbeing plays a central role in quality of life and encompasses aspects pertaining to mental and social wellbeing, as well as physical wellbeing and the absence of disease. Building upon the natural human understanding that smiling is an expression of happiness, studies have shown that the process of smiling in a genuine manner can help to improve an individual's happiness. To date, approaches that measure happiness have relied upon subjective self-assessment using one of a wide range of questionnaires. However, more recently, Affective Technology has emerged that provides the potential to move towards a more objective assessment of happiness. This paper describes a proposed study aimed at evaluating a bespoke smartphone-based affective technology that attempts to promote happiness through smiling, by reminding participants to smile on a regular basis.
{"title":"Remember to smile: design of a mobile affective technology to help promote individual happiness through smiling","authors":"G. Moore, L. Galway, M. Donnelly","doi":"10.1145/3154862.3154936","DOIUrl":"https://doi.org/10.1145/3154862.3154936","url":null,"abstract":"Wellbeing plays a central role in quality of life and encompasses aspects pertaining to mental and social wellbeing, as well as physical wellbeing and the absence of disease. Building upon the natural human understanding that smiling is an expression of happiness, studies have shown that the process of smiling in a genuine manner can help to improve an individual's happiness. To date, approaches that measure happiness have relied upon subjective self-assessment using one of a wide range of questionnaires. However, more recently, Affective Technology has emerged that provides the potential to move towards a more objective assessment of happiness. This paper describes a proposed study aimed at evaluating a bespoke smartphone-based affective technology that attempts to promote happiness through smiling, by reminding participants to smile on a regular basis.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126599898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Physical activity yields affective benefits like mood improvement and a sense of accomplishment or a general sense of feeling good. However, existing interventions to promote physical activity typically do not make tracking or visualization of affective benefits a prominent part of the interface. We conducted a survey asking people about physical activity episodes that made them feel good and the impact of those episodes on their exercise intentions. We found that the affective benefits of exercise motivated respondents to become more active. In this paper, we report on the affective benefits that resulted from exercise, what users perceived as causing those affective benefits, and what impact feeling good from being active had on their intentions for future exercise. We discuss the implications of our findings for the design of interventions that use affective benefits to promote physical activity.
{"title":"\"Move into another world of happy\": insights for designing affect-based physical activity interventions","authors":"Sonali R. Mishra, P. Klasnja","doi":"10.1145/3154862.3154880","DOIUrl":"https://doi.org/10.1145/3154862.3154880","url":null,"abstract":"Physical activity yields affective benefits like mood improvement and a sense of accomplishment or a general sense of feeling good. However, existing interventions to promote physical activity typically do not make tracking or visualization of affective benefits a prominent part of the interface. We conducted a survey asking people about physical activity episodes that made them feel good and the impact of those episodes on their exercise intentions. We found that the affective benefits of exercise motivated respondents to become more active. In this paper, we report on the affective benefits that resulted from exercise, what users perceived as causing those affective benefits, and what impact feeling good from being active had on their intentions for future exercise. We discuss the implications of our findings for the design of interventions that use affective benefits to promote physical activity.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130907875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gabriel Lugo Bustillo, M. Ibarra-Manzano, F. Ba, I. Cheng
In this paper, we take advantage of the free hand interaction technology as a medical tool, either in rehabilitation centers or at home, that allows the evaluation of patients with Parkinson's. We have created a virtual reality scene to engage the patient to feel in an activity that can be found in daily life, and use the Leap Motion controller tracking to evaluate and classify the tremor in the hands. A sample of 33 patients diagnosed with Parkinson's disease (PD) participated in the study. Three tests were performed per patient, the first two to evaluate the amplitude of the postural tremor in each hand, and the third to measure the time to complete a specific task. Analysis shows that our tool can be used effectively to classify the stage of Parkinson's disease.
{"title":"Virtual reality and hand tracking system as a medical tool to evaluate patients with parkinson's","authors":"Gabriel Lugo Bustillo, M. Ibarra-Manzano, F. Ba, I. Cheng","doi":"10.1145/3154862.3154924","DOIUrl":"https://doi.org/10.1145/3154862.3154924","url":null,"abstract":"In this paper, we take advantage of the free hand interaction technology as a medical tool, either in rehabilitation centers or at home, that allows the evaluation of patients with Parkinson's. We have created a virtual reality scene to engage the patient to feel in an activity that can be found in daily life, and use the Leap Motion controller tracking to evaluate and classify the tremor in the hands. A sample of 33 patients diagnosed with Parkinson's disease (PD) participated in the study. Three tests were performed per patient, the first two to evaluate the amplitude of the postural tremor in each hand, and the third to measure the time to complete a specific task. Analysis shows that our tool can be used effectively to classify the stage of Parkinson's disease.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evidence-based health promotion programs implement clinical practice guidelines built upon results of clinical trials with a definite number of participants, collected during a specific period of time. Wearable technologies allow for continuous observation of wellness parameters of multiple citizens, combined with monitoring of activities and context parameters involved in citizens' wellness. A statistical inference model can describe the relation between multidimensional activities and context parameters, the wellness of an individual and a comparable reference group, utilizing machine learning techniques and knowledge from continuous observations of multiple citizens. This paper presents a holistic concept of a coach system, namely eCoach, that combines specialized medical evidence available from randomized control trials, with individual and reference knowledge to create and reinforce wellness-based recommendations. The eCoach adapts these recommendations in a continuous personalized coaching dialog addressing citizen's needs and preferences.
{"title":"Conceptualization of a personalized ecoach for wellness promotion","authors":"Martin W. Gerdes, S. Martinez, D. Tjondronegoro","doi":"10.1145/3154862.3154930","DOIUrl":"https://doi.org/10.1145/3154862.3154930","url":null,"abstract":"Evidence-based health promotion programs implement clinical practice guidelines built upon results of clinical trials with a definite number of participants, collected during a specific period of time. Wearable technologies allow for continuous observation of wellness parameters of multiple citizens, combined with monitoring of activities and context parameters involved in citizens' wellness. A statistical inference model can describe the relation between multidimensional activities and context parameters, the wellness of an individual and a comparable reference group, utilizing machine learning techniques and knowledge from continuous observations of multiple citizens. This paper presents a holistic concept of a coach system, namely eCoach, that combines specialized medical evidence available from randomized control trials, with individual and reference knowledge to create and reinforce wellness-based recommendations. The eCoach adapts these recommendations in a continuous personalized coaching dialog addressing citizen's needs and preferences.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124778620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the area of advanced human-computer interaction, automatic gesture recognition is an important field. Motion data produced by the accelerometer of a smart watch can be utilized in hand gesture recognition. In this work we examine the use of a commodity smart watch and a smartphone as the capture and the processing units respectively, for recognizing gestures. We claim that if the proper gesture recognition algorithms are applied, the recognition of natural gestures i.e. 3-D gestures easily performed by an individual can be accurate enough to be useful in everyday life activities. Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) methodologies are utilized in this context and evaluated using a set of six 3-D natural gestures.
{"title":"Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data","authors":"A. Mezari, Ilias Maglogiannis","doi":"10.1145/3154862.3154927","DOIUrl":"https://doi.org/10.1145/3154862.3154927","url":null,"abstract":"In the area of advanced human-computer interaction, automatic gesture recognition is an important field. Motion data produced by the accelerometer of a smart watch can be utilized in hand gesture recognition. In this work we examine the use of a commodity smart watch and a smartphone as the capture and the processing units respectively, for recognizing gestures. We claim that if the proper gesture recognition algorithms are applied, the recognition of natural gestures i.e. 3-D gestures easily performed by an individual can be accurate enough to be useful in everyday life activities. Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) methodologies are utilized in this context and evaluated using a set of six 3-D natural gestures.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For decades, the air pollution has been studied as key driver factor for uncountable number of diseases ranging from respiratory diseases to neoplasms. However, in each city, the effort to control the air quality is low. Plenty of studies report the importance of quality of air, but majority of them is based on outdoors air quality that do not consider or track people outside or inside a building. In this study, we have analyzed the largest electronic medical records (EMR) in New York City and air pollution data collected from environmental protection agency (EPA) to identify environmental diseases impacted by air pollution. We have identified that the different environmental diseases are significantly enriched to certain geographic areas influenced by surrounding environment. Therefore, using this data-driven approach, we are here to present a new Internet of Things network concept. The new architecture based on LoRaWAN has the objective to bypass most of the issues encountered in these years to collect patient data as well as to improve the telemedicine. At the same time, the network can open new scenario of crowdsourcing to improve the granularity of data collection. Third-party companies can use IoT infrastructure to test new devices and to integrate the existing data sets.
{"title":"Application of I-COMO device towards geographic disease enrichment pattern revealed from electronic medical record at a large Urban academic medical center","authors":"M. Danieletto, Li Li, J. Dudley","doi":"10.1145/3154862.3154913","DOIUrl":"https://doi.org/10.1145/3154862.3154913","url":null,"abstract":"For decades, the air pollution has been studied as key driver factor for uncountable number of diseases ranging from respiratory diseases to neoplasms. However, in each city, the effort to control the air quality is low. Plenty of studies report the importance of quality of air, but majority of them is based on outdoors air quality that do not consider or track people outside or inside a building. In this study, we have analyzed the largest electronic medical records (EMR) in New York City and air pollution data collected from environmental protection agency (EPA) to identify environmental diseases impacted by air pollution. We have identified that the different environmental diseases are significantly enriched to certain geographic areas influenced by surrounding environment. Therefore, using this data-driven approach, we are here to present a new Internet of Things network concept. The new architecture based on LoRaWAN has the objective to bypass most of the issues encountered in these years to collect patient data as well as to improve the telemedicine. At the same time, the network can open new scenario of crowdsourcing to improve the granularity of data collection. Third-party companies can use IoT infrastructure to test new devices and to integrate the existing data sets.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123235945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florian Schmidt, Vincent Hennig, Sarah Köhler, Marcel Wallschläger, Anton Gulenko, Hartmut Schmidt, O. Kao
Information overload in the medical field is both visible by the increased number of publications as well as by the volume of patient data. In order to cope with this problem, we propose a novel framework combining patient's health records with medical knowledge, which is based on medical algorithms from frequently used guidelines. The framework uses new types of animation and layout algorithms for visualizing knowledge models in health records. At the Münster University Hospital the framework is already in prototypical use for education and communication purposes.
{"title":"Novel framework combining health records with medical algorithms","authors":"Florian Schmidt, Vincent Hennig, Sarah Köhler, Marcel Wallschläger, Anton Gulenko, Hartmut Schmidt, O. Kao","doi":"10.1145/3154862.3154902","DOIUrl":"https://doi.org/10.1145/3154862.3154902","url":null,"abstract":"Information overload in the medical field is both visible by the increased number of publications as well as by the volume of patient data. In order to cope with this problem, we propose a novel framework combining patient's health records with medical knowledge, which is based on medical algorithms from frequently used guidelines. The framework uses new types of animation and layout algorithms for visualizing knowledge models in health records. At the Münster University Hospital the framework is already in prototypical use for education and communication purposes.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123645253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ayse G. Büyüktür, M. Ackerman, Mark W. Newman, Pei-Yao Hung
Self-care in Spinal Cord Injury (SCI) is highly complex and individualized. Patients struggle to adapt to life with SCI, especially when they go home after rehabilitation. We conducted a field study to understand how self-care plans work for patients in their lived experience and what requirements there might be for an augmentative system. We found that patients develop their own self-care plans over time, and that routinization plays a key role in SCI self-care. Importantly, self-care activities exist in different states of routinization that have implications for the technological support that should be provided. Our findings suggest that self-care can be supported by different types of semi-automated tracking that account for the different routinization of activities, the collaborative nature of care, and the life-long, dynamic nature of this condition. The findings from our study also extend recent guidelines for semi-automated tracking in health.
{"title":"Design considerations for semi-automated tracking: self-care plans in spinal cord injury","authors":"Ayse G. Büyüktür, M. Ackerman, Mark W. Newman, Pei-Yao Hung","doi":"10.1145/3154862.3154870","DOIUrl":"https://doi.org/10.1145/3154862.3154870","url":null,"abstract":"Self-care in Spinal Cord Injury (SCI) is highly complex and individualized. Patients struggle to adapt to life with SCI, especially when they go home after rehabilitation. We conducted a field study to understand how self-care plans work for patients in their lived experience and what requirements there might be for an augmentative system. We found that patients develop their own self-care plans over time, and that routinization plays a key role in SCI self-care. Importantly, self-care activities exist in different states of routinization that have implications for the technological support that should be provided. Our findings suggest that self-care can be supported by different types of semi-automated tracking that account for the different routinization of activities, the collaborative nature of care, and the life-long, dynamic nature of this condition. The findings from our study also extend recent guidelines for semi-automated tracking in health.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125016047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Petar Velickovic, N. Lane, S. Bhattacharya, A. Chieh, O. Bellahsen, M. Vegreville
People are naturally sensitive to the sharing of their health data collected by various connected consumer devices (e.g., smart scales, sleep trackers) with third parties. However, sharing this data to compute aggregate statistics and comparisons is a basic building block for a range of medical studies based on large-scale consumer devices; such studies have the potential to transform how we study disease and behavior. Furthermore, informing users as to how their health measurements and activities compare with friends, demographic peers and globally has been shown to be a powerful tool for behavior change and management in individuals. While experienced organizations can safely perform aggregate user health analysis, there is a significant need for new privacy-preserving mechanisms that enable people to engage in the same way even with untrusted third parties (e.g., small/recently established organizations). In this work, we propose a new approach to this problem grounded in the use of deep distributed behavior models. These are discriminative deep learning models that can approximate the calculation of various aggregate functions. Models are bootstrapped with training data from a modestly sized cohort and then distributed directly to personal devices to estimate, for example, how the user (perhaps in terms of daily step counts) ranks/compares to various demographics ranges (like age and sex). Critically, the user's own data now never has to leave the device. We validate this method using a 1.2M-user 22-month dataset that spans body-weight, sleep hours and step counts collected by devices from Nokia Digital Health - Withings. Experiments show our framework remains accurate for a range of commonly used statistical aggregate functions. This result opens a powerful new paradigm for privacy-preserving analytics under which user data largely remains on personal devices, overcoming a variety of potential privacy risks.
{"title":"Scaling health analytics to millions without compromising privacy using deep distributed behavior models","authors":"Petar Velickovic, N. Lane, S. Bhattacharya, A. Chieh, O. Bellahsen, M. Vegreville","doi":"10.1145/3154862.3154873","DOIUrl":"https://doi.org/10.1145/3154862.3154873","url":null,"abstract":"People are naturally sensitive to the sharing of their health data collected by various connected consumer devices (e.g., smart scales, sleep trackers) with third parties. However, sharing this data to compute aggregate statistics and comparisons is a basic building block for a range of medical studies based on large-scale consumer devices; such studies have the potential to transform how we study disease and behavior. Furthermore, informing users as to how their health measurements and activities compare with friends, demographic peers and globally has been shown to be a powerful tool for behavior change and management in individuals. While experienced organizations can safely perform aggregate user health analysis, there is a significant need for new privacy-preserving mechanisms that enable people to engage in the same way even with untrusted third parties (e.g., small/recently established organizations). In this work, we propose a new approach to this problem grounded in the use of deep distributed behavior models. These are discriminative deep learning models that can approximate the calculation of various aggregate functions. Models are bootstrapped with training data from a modestly sized cohort and then distributed directly to personal devices to estimate, for example, how the user (perhaps in terms of daily step counts) ranks/compares to various demographics ranges (like age and sex). Critically, the user's own data now never has to leave the device. We validate this method using a 1.2M-user 22-month dataset that spans body-weight, sleep hours and step counts collected by devices from Nokia Digital Health - Withings. Experiments show our framework remains accurate for a range of commonly used statistical aggregate functions. This result opens a powerful new paradigm for privacy-preserving analytics under which user data largely remains on personal devices, overcoming a variety of potential privacy risks.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：