Activity recognition using wearable motion sensors plays an important role in pervasive wellness and healthcare monitoring applications. The activity recognition algorithms are often designed to work with a known orientation of sensors on the body. In the case of accidental displacement of the motion sensors, it is important to identify the new sensor location and orientation. This step, often called calibration or recalibration, requires extra effort from the user to either perform a set of known movements, or enter information about the placement of the sensors manually. In this paper, we propose a camera-assisted calibration approach that does not require any extra effort from the user. The calibration is done seamlessly when the user appears in front of the camera (in our case, a Kinect camera) and performs an arbitrary activity of choice (e.g., walking in front of the camera). We provide experimental results supporting the effectiveness of our approach.
{"title":"Zero-Effort Camera-Assisted Calibration Techniques for Wearable Motion Sensors.","authors":"Jian Wu, Roozbeh Jafari","doi":"10.1145/2668883.2668888","DOIUrl":"https://doi.org/10.1145/2668883.2668888","url":null,"abstract":"<p><p>Activity recognition using wearable motion sensors plays an important role in pervasive wellness and healthcare monitoring applications. The activity recognition algorithms are often designed to work with a <i>known</i> orientation of sensors on the body. In the case of accidental displacement of the motion sensors, it is important to identify the new sensor location and orientation. This step, often called <i>calibration</i> or <i>recalibration</i>, requires extra effort from the user to either perform a set of known movements, or enter information about the placement of the sensors manually. In this paper, we propose a camera-assisted calibration approach that does not require any extra effort from the user. The calibration is done seamlessly when the user appears in front of the camera (in our case, a Kinect camera) and performs an arbitrary activity of choice (<i>e.g.</i>, walking in front of the camera). We provide experimental results supporting the effectiveness of our approach.</p>","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"2014 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2668883.2668888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34766304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vincent M. Stanford, Lukas L. Diduch, A. Fillinger, K. Sayrafian-Pour
The Integrated Clinical Environment (ICE) ASTM-2761 Standard specifies an architecture for real-time medical device interoperability, and a set of Clinical Concepts of Operations (CConOps). Based on an analysis of the CConOps, all showing improved patient safety, we developed an ICE prototype reflecting the ICE Synchronization with Safety Interlock Scenario, but with no risk to human participants, using wireless Medical Devices of different vendors.
{"title":"Connecting medical devices through ASTM-2761-09: schedule conflict detection prototype","authors":"Vincent M. Stanford, Lukas L. Diduch, A. Fillinger, K. Sayrafian-Pour","doi":"10.1145/2534088.2534100","DOIUrl":"https://doi.org/10.1145/2534088.2534100","url":null,"abstract":"The Integrated Clinical Environment (ICE) ASTM-2761 Standard specifies an architecture for real-time medical device interoperability, and a set of Clinical Concepts of Operations (CConOps). Based on an analysis of the CConOps, all showing improved patient safety, we developed an ICE prototype reflecting the ICE Synchronization with Safety Interlock Scenario, but with no risk to human participants, using wireless Medical Devices of different vendors.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"50 10","pages":"20:1-20:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91464548","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}
J. Milazzo, P. Bagade, Ayan Banerjee, Sandeep K. S. Gupta
We demonstrate bHealthy, a physiological feedback-based mobile wellness application suite. bHealthy, monitors physiological signals using electrocardiogram, electroencephalogram, and accelerometer sensors; uses a suite of assessment applications to detect mental state of the user; suggests apps to enhance wellbeing; and tracks the performance of the user in the suggested apps. bHealthy also provides wellness reports based on the user's activity in apps over a period of time.
{"title":"bHealthy: a physiological feedback-based mobile wellness application suite","authors":"J. Milazzo, P. Bagade, Ayan Banerjee, Sandeep K. S. Gupta","doi":"10.1145/2534088.2534095","DOIUrl":"https://doi.org/10.1145/2534088.2534095","url":null,"abstract":"We demonstrate bHealthy, a physiological feedback-based mobile wellness application suite. bHealthy, monitors physiological signals using electrocardiogram, electroencephalogram, and accelerometer sensors; uses a suite of assessment applications to detect mental state of the user; suggests apps to enhance wellbeing; and tracks the performance of the user in the suggested apps. bHealthy also provides wellness reports based on the user's activity in apps over a period of time.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"19 1","pages":"14:1-14:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89970353","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 this paper, we describe a mobile point of care system designed to improve the healthcare workflow. We have created a rapid diagnostic test reader that can interpret the results from lateral flow point of care tests. Our approach exploits the use of mobile technology and cloud based services to closely integrate the clinic with the community.
{"title":"A mobile point of care reader for immediate diagnostics and analysis","authors":"Phillip Olla, Tatu Prykari, H. Kauniskangas","doi":"10.1145/2534088.2534096","DOIUrl":"https://doi.org/10.1145/2534088.2534096","url":null,"abstract":"In this paper, we describe a mobile point of care system designed to improve the healthcare workflow. We have created a rapid diagnostic test reader that can interpret the results from lateral flow point of care tests. Our approach exploits the use of mobile technology and cloud based services to closely integrate the clinic with the community.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"1 1","pages":"18:1-18:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84973497","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}
Pain is a leading cause of discomfort and loss of efficiency, with a total of 100 million people in the United States of America suffering from acute and chronic pain conditions [1]. In many types of pain conditions, it is not possible to completely alleviate the symptoms; hence there is a need to develop techniques to manage pain effectively. Some of the clinically used pain management tools are paper based, which is cumbersome. Hence we propose a cloud based universal pain management system. Our system is designed to collect data from users about the location and type of pain experienced by them and gives clinical interventions if the pain levels are greater than a personalized threshold for an extended duration. Pilot results have demonstrated that the usability levels of a portion of our system (SMS). Following IRB approval, we hope to recruit a total of 60 patients with four different causes of pain from Emory pain clinic to show usability of the complete system.
{"title":"Cloud-based integrative solution for personalized pain management","authors":"Janani Venugopalan, Chihwen Cheng, May D. Wang","doi":"10.1145/2534088.2534094","DOIUrl":"https://doi.org/10.1145/2534088.2534094","url":null,"abstract":"Pain is a leading cause of discomfort and loss of efficiency, with a total of 100 million people in the United States of America suffering from acute and chronic pain conditions [1]. In many types of pain conditions, it is not possible to completely alleviate the symptoms; hence there is a need to develop techniques to manage pain effectively. Some of the clinically used pain management tools are paper based, which is cumbersome. Hence we propose a cloud based universal pain management system. Our system is designed to collect data from users about the location and type of pain experienced by them and gives clinical interventions if the pain levels are greater than a personalized threshold for an extended duration. Pilot results have demonstrated that the usability levels of a portion of our system (SMS). Following IRB approval, we hope to recruit a total of 60 patients with four different causes of pain from Emory pain clinic to show usability of the complete system.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"144 1","pages":"21:1-21:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81795917","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}
Healthcare organizations are looking to implement mobile health applications that significantly improve healthcare delivery, yet adhere to existing health information privacy and security rules and regulations. However, these same organizations are struggling to find comprehensive frameworks, guidelines, and examples on how to successfully accomplish these interrelated goals. This paper presents a set of guiding principles specific to designing and building practitioner oriented mHealth applications. The system design is described, including the security features that were implemented, and results from performance testing in a live field test environment on 20 ambulances and 7 hospitals.
{"title":"A secure mHealth application for EMS: design and implementation","authors":"Abdullah Murad, Benjamin L. Schooley, Yousef Abed","doi":"10.1145/2534088.2534103","DOIUrl":"https://doi.org/10.1145/2534088.2534103","url":null,"abstract":"Healthcare organizations are looking to implement mobile health applications that significantly improve healthcare delivery, yet adhere to existing health information privacy and security rules and regulations. However, these same organizations are struggling to find comprehensive frameworks, guidelines, and examples on how to successfully accomplish these interrelated goals. This paper presents a set of guiding principles specific to designing and building practitioner oriented mHealth applications. The system design is described, including the security features that were implemented, and results from performance testing in a live field test environment on 20 ambulances and 7 hospitals.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"38 1","pages":"15:1-15:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85953330","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}
Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily Physical Activity (PA) patterns affect health. Mobile phones and wearable sensors (e.g. accelerometers (ACC) and heart rate (HR) monitors) have been widely used to monitor PA. In this paper we present a real-time implementation of activity-specific EE estimation algorithms, using an Health Patch and an iPhone. Our approach to continuous monitoring of PA targets personalized behavior and health status assessment, by automatically accounting for a person's cardiorespiratory fitness level (CRF), which is the main cause of inter-individual variation in HR during moderate to vigorous activities. The proposed system opens new opportunities for personalized health assessment in daily life, using ubiquitous devices.
{"title":"Personalized physical activity monitoring on the move","authors":"M. Altini, J. Penders, R. Vullers, O. Amft","doi":"10.1145/2534088.2534092","DOIUrl":"https://doi.org/10.1145/2534088.2534092","url":null,"abstract":"Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily Physical Activity (PA) patterns affect health. Mobile phones and wearable sensors (e.g. accelerometers (ACC) and heart rate (HR) monitors) have been widely used to monitor PA. In this paper we present a real-time implementation of activity-specific EE estimation algorithms, using an Health Patch and an iPhone. Our approach to continuous monitoring of PA targets personalized behavior and health status assessment, by automatically accounting for a person's cardiorespiratory fitness level (CRF), which is the main cause of inter-individual variation in HR during moderate to vigorous activities. The proposed system opens new opportunities for personalized health assessment in daily life, using ubiquitous devices.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"9 1","pages":"8:1-8:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87265020","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}
O. Dehzangi, Zheng Zhao, Mohammad-Mahdi Bidmeshki, John Biggan, Christopher Ray, R. Jafari
Gait and postural control are important aspects of human movement and balance. Normal movement control in human is subject to change with aging when the nervous system, comprising somatosensory, visual senses, spatial orientation senses, and neuromuscular control starts to degrade. As a result, the body movement control such as the lateral sway while walking is affected which has been shown to be a significant cause of falling among the elderly. Biofeedback has been investigated to assist elderly improve their body movement and postural ability, by supplementing the feedback to the nervous system. In this paper, we propose a wearable low-power sensor system capable of characterizing lateral sway and gait parameters. Then, it can provide corrective feedback to reduce excessive sway in real-time via vibratory feedback modules. Real-time and low-power characteristics along with wearability of our proposed system allow long-term continuous subjects' sway monitoring while giving direct feedback to enhance walking sway and prevent falling. It can also be used in the clinics as a tool for evaluating the risks of falls, and training users to better maintain their balance. The effectiveness of the biofeedback system was evaluated on 12 older adults as they performed gait and stance tasks with and without biofeedback. Significant improvement (p-value < 0.1) in sway angle in variance of the sway angle, variance of gait phases, and in postural control while on perturbed surface was detected when the proposed sway error feedback system was used.
{"title":"The impact of vibrotactile biofeedback on the excessive walking sway and the postural control in elderly","authors":"O. Dehzangi, Zheng Zhao, Mohammad-Mahdi Bidmeshki, John Biggan, Christopher Ray, R. Jafari","doi":"10.1145/2534088.2534110","DOIUrl":"https://doi.org/10.1145/2534088.2534110","url":null,"abstract":"Gait and postural control are important aspects of human movement and balance. Normal movement control in human is subject to change with aging when the nervous system, comprising somatosensory, visual senses, spatial orientation senses, and neuromuscular control starts to degrade. As a result, the body movement control such as the lateral sway while walking is affected which has been shown to be a significant cause of falling among the elderly. Biofeedback has been investigated to assist elderly improve their body movement and postural ability, by supplementing the feedback to the nervous system. In this paper, we propose a wearable low-power sensor system capable of characterizing lateral sway and gait parameters. Then, it can provide corrective feedback to reduce excessive sway in real-time via vibratory feedback modules. Real-time and low-power characteristics along with wearability of our proposed system allow long-term continuous subjects' sway monitoring while giving direct feedback to enhance walking sway and prevent falling. It can also be used in the clinics as a tool for evaluating the risks of falls, and training users to better maintain their balance. The effectiveness of the biofeedback system was evaluated on 12 older adults as they performed gait and stance tasks with and without biofeedback. Significant improvement (p-value < 0.1) in sway angle in variance of the sway angle, variance of gait phases, and in postural control while on perturbed surface was detected when the proposed sway error feedback system was used.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77696097","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}
Ensuring security of private health data over the communication channel from the sensors to the back-end medical cloud is crucial in a mHealth system. This end-to-end (E2E) security is enabled by distributing cryptographic keys between a sensor and the cloud so that the data can be encrypted and its integrity protected. Further, the key can also be used for mutually authenticating the communication. The distribution of keys is one of the biggest overheads in enabling secure communication and needs to be done is a transparent way that minimizes the cognitive load on the users (patients). Traditional approaches for providing E2E security for mHealth systems are based on asymmetric cryptosystems that require extensive security infrastructure. In this paper, we propose a novel protocol, Physiology-based End-to-End Security (PEES), which provides a secure communication channel between the sensors and the back-end medical cloud in a transparent way. PEES uses: (1) physiological signal features to hide a secret key, and (2) synthetically generated physiological signals from generative models parameterized with patient's physiological information, to unhide the key. Moreover, in PEES authentication comes for free since only sensors on the user's body has access to physiological features and can therefore gain access to the protected information in the cloud. The analysis of the approach using electrocardiogram (ECG) and phototplethysmogram (PPG) signals and their associated models demonstrate the feasibility of PEES. The protocol is light-weight for sensors and has no pre-deployment or storage requirements and can provide strong and random keys (≈ 90 bits long). We have also started clinical studies to establish its efficacy in practice.
{"title":"PEES: physiology-based end-to-end security for mHealth","authors":"Ayan Banerjee, S. Gupta, K. Venkatasubramanian","doi":"10.1145/2534088.2534109","DOIUrl":"https://doi.org/10.1145/2534088.2534109","url":null,"abstract":"Ensuring security of private health data over the communication channel from the sensors to the back-end medical cloud is crucial in a mHealth system. This end-to-end (E2E) security is enabled by distributing cryptographic keys between a sensor and the cloud so that the data can be encrypted and its integrity protected. Further, the key can also be used for mutually authenticating the communication. The distribution of keys is one of the biggest overheads in enabling secure communication and needs to be done is a transparent way that minimizes the cognitive load on the users (patients). Traditional approaches for providing E2E security for mHealth systems are based on asymmetric cryptosystems that require extensive security infrastructure. In this paper, we propose a novel protocol, Physiology-based End-to-End Security (PEES), which provides a secure communication channel between the sensors and the back-end medical cloud in a transparent way. PEES uses: (1) physiological signal features to hide a secret key, and (2) synthetically generated physiological signals from generative models parameterized with patient's physiological information, to unhide the key. Moreover, in PEES authentication comes for free since only sensors on the user's body has access to physiological features and can therefore gain access to the protected information in the cloud. The analysis of the approach using electrocardiogram (ECG) and phototplethysmogram (PPG) signals and their associated models demonstrate the feasibility of PEES. The protocol is light-weight for sensors and has no pre-deployment or storage requirements and can provide strong and random keys (≈ 90 bits long). We have also started clinical studies to establish its efficacy in practice.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"26 1","pages":"2:1-2:8"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73941697","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 (PA) is one of the most important determinants of health. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. In this paper we investigate the benefit of multiple physiological signals (Heart Rate (HR), respiration rate, Galvanic Skin Response (GSR), skin humidity) as well as accelerometer (ACC) data from two locations (wrist - combining ACC, GSR and skin humidity - and chest - combining ACC and HR) on PA type and EE estimation. We implemented single regression, activity recognition and activity-specific EE models on data collected from 16 subjects, while performing a set of PAs, grouped into six clusters (lying, sedentary, dynamic, walking, biking and running). Our results show that combining ACC and physiological signals improves performance for activity recognition (by 2 and 8% for the chest and wrist) and EE (by 36 - chest - and 35% - wrist - for single regression models, and by 18 - chest - and 46% - wrist - for activity-specific models). Physiological signals other than HR showed a coarser relation with level of physical exertion, resulting in being better predictors of activity cluster type and separation between inactivity and activity than EE, due to the weak correlation to EE within an activity cluster.
{"title":"Combining wearable accelerometer and physiological data for activity and energy expenditure estimation","authors":"M. Altini, J. Penders, R. Vullers, O. Amft","doi":"10.1145/2534088.2534106","DOIUrl":"https://doi.org/10.1145/2534088.2534106","url":null,"abstract":"Physical Activity (PA) is one of the most important determinants of health. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. In this paper we investigate the benefit of multiple physiological signals (Heart Rate (HR), respiration rate, Galvanic Skin Response (GSR), skin humidity) as well as accelerometer (ACC) data from two locations (wrist - combining ACC, GSR and skin humidity - and chest - combining ACC and HR) on PA type and EE estimation. We implemented single regression, activity recognition and activity-specific EE models on data collected from 16 subjects, while performing a set of PAs, grouped into six clusters (lying, sedentary, dynamic, walking, biking and running). Our results show that combining ACC and physiological signals improves performance for activity recognition (by 2 and 8% for the chest and wrist) and EE (by 36 - chest - and 35% - wrist - for single regression models, and by 18 - chest - and 46% - wrist - for activity-specific models). Physiological signals other than HR showed a coarser relation with level of physical exertion, resulting in being better predictors of activity cluster type and separation between inactivity and activity than EE, due to the weak correlation to EE within an activity cluster.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"24 1","pages":"1:1-1:8"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87622126","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}
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