Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for impr...
{"title":"Human Data Model","authors":"MäkitaloNiko, Flores-MartinDaniel, FloresHuber, LagerspetzEemil, ChristopheFrancois, IhantolaPetri, BabazadehMasiar, HuiPan, MurilloJuan Manuel, TarkomaSasu, MikkonenTommi","doi":"10.1145/3402524","DOIUrl":"https://doi.org/10.1145/3402524","url":null,"abstract":"Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for impr...","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1-39"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3402524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64029070","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}
This article considers detecting eating in free-living humans by tracking wrist motion. We are specifically interested in the effect of secondary activities that people conduct while simultaneously...
{"title":"The Impact of Walking and Resting on Wrist Motion for Automated Detection of Meals","authors":"SharmaSurya, JasperPhillip, MuthEric, HooverAdam","doi":"10.1145/3407623","DOIUrl":"https://doi.org/10.1145/3407623","url":null,"abstract":"This article considers detecting eating in free-living humans by tracking wrist motion. We are specifically interested in the effect of secondary activities that people conduct while simultaneously...","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3407623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64031026","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}
Aviv Elor, Michael Powell, Evanjelin Mahmoodi, Nico Hawthorne, M. Teodorescu, S. Kurniawan
Inactivity and a lack of engagement with exercise is a pressing health problem in the United States and beyond. Immersive Virtual Reality (iVR) is a promising medium to motivate users through engaging virtual environments. Currently, modern iVR lacks a comparative analysis between research and consumer-grade systems for exercise and health. This article examines two such iVR mediums: the Cave Automated Virtual Environment (CAVE) and the head-mounted display (HMD). Specifically, we compare the room-scale Mechdyne CAVE and HTC Vive Pro HMD with a custom in-house exercise game that was designed such that user experiences were as consistent as possible between both systems. To ensure that our findings are generalizable for users of varying abilities, we recruited 40 participants with and without cognitive disabilities with regard to the fact that iVR environments and games can differ in their cognitive challenge between users. Our results show that across all abilities, the HMD excelled in in-game performance, biofeedback response, and player engagement. We conclude with considerations in utilizing iVR systems for exergaming with users across cognitive abilities.
在美国和其他国家，缺乏运动和缺乏锻炼是一个紧迫的健康问题。沉浸式虚拟现实(iVR)是一种很有前途的媒介，可以通过参与虚拟环境来激励用户。目前，现代iVR缺乏研究和消费级运动和健康系统之间的比较分析。本文研究了两种iVR媒介:Cave自动化虚拟环境(Cave)和头戴式显示器(HMD)。具体来说，我们将房间大小的Mechdyne CAVE和HTC Vive Pro HMD与一款定制的内部运动游戏进行了比较，这款游戏的设计使两个系统之间的用户体验尽可能一致。为了确保我们的发现适用于不同能力的用户，我们招募了40名有或没有认知障碍的参与者，考虑到iVR环境和游戏在用户之间的认知挑战可能不同。我们的研究结果显示，在所有能力中，HMD在游戏中的表现、生物反馈反应和玩家粘性方面都表现出色。我们总结了利用iVR系统与不同认知能力的用户进行游戏的考虑。
{"title":"On Shooting Stars","authors":"Aviv Elor, Michael Powell, Evanjelin Mahmoodi, Nico Hawthorne, M. Teodorescu, S. Kurniawan","doi":"10.1145/3396249","DOIUrl":"https://doi.org/10.1145/3396249","url":null,"abstract":"Inactivity and a lack of engagement with exercise is a pressing health problem in the United States and beyond. Immersive Virtual Reality (iVR) is a promising medium to motivate users through engaging virtual environments. Currently, modern iVR lacks a comparative analysis between research and consumer-grade systems for exercise and health. This article examines two such iVR mediums: the Cave Automated Virtual Environment (CAVE) and the head-mounted display (HMD). Specifically, we compare the room-scale Mechdyne CAVE and HTC Vive Pro HMD with a custom in-house exercise game that was designed such that user experiences were as consistent as possible between both systems. To ensure that our findings are generalizable for users of varying abilities, we recruited 40 participants with and without cognitive disabilities with regard to the fact that iVR environments and games can differ in their cognitive challenge between users. Our results show that across all abilities, the HMD excelled in in-game performance, biofeedback response, and player engagement. We conclude with considerations in utilizing iVR systems for exergaming with users across cognitive abilities.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1 - 22"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3396249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43144877","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}
Chun-Tung Li, Jiannong Cao, Xuefeng Liu, M. Stojmenovic
A successive similar pattern (SSP) is a series of similar sequences that occur consecutively at non-regular intervals in time series. Mining SSPs could provide valuable information without a priori knowledge, which is crucial in many applications ranging from health monitoring to activity recognition. However, most existing work is computationally expensive, focuses only on periodic patterns occurring in regular time intervals, and is unable to recognize patterns containing multiple periods. Here we investigate a more general problem of finding similar patterns occurring successively, in which the similarity between patterns is measured by the z-normalized Euclidean distance. We propose a linear time, robust method, called Multiple-length Successive sIMilar PAtterns Detector (mSIMPAD), that mines SSPs of multiple lengths, making no assumptions regarding periodicity. We apply our method on the detection of repetitive movement using a wearable inertial measurement unit. The experiments were conducted on three public datasets, two of which contain simple walking and idle data, whereas the third is more complex and contains multiple activities. mSIMPAD achieved F-score improvements of 3.2% and 6.5%, respectively, over the simple and complex datasets compared to the state-of-the-art walking detector. In addition, mSIMPAD is scalable and applicable to real-time applications since it operates in linear time complexity.
{"title":"mSIMPAD","authors":"Chun-Tung Li, Jiannong Cao, Xuefeng Liu, M. Stojmenovic","doi":"10.1145/3396250","DOIUrl":"https://doi.org/10.1145/3396250","url":null,"abstract":"A successive similar pattern (SSP) is a series of similar sequences that occur consecutively at non-regular intervals in time series. Mining SSPs could provide valuable information without a priori knowledge, which is crucial in many applications ranging from health monitoring to activity recognition. However, most existing work is computationally expensive, focuses only on periodic patterns occurring in regular time intervals, and is unable to recognize patterns containing multiple periods. Here we investigate a more general problem of finding similar patterns occurring successively, in which the similarity between patterns is measured by the z-normalized Euclidean distance. We propose a linear time, robust method, called Multiple-length Successive sIMilar PAtterns Detector (mSIMPAD), that mines SSPs of multiple lengths, making no assumptions regarding periodicity. We apply our method on the detection of repetitive movement using a wearable inertial measurement unit. The experiments were conducted on three public datasets, two of which contain simple walking and idle data, whereas the third is more complex and contains multiple activities. mSIMPAD achieved F-score improvements of 3.2% and 6.5%, respectively, over the simple and complex datasets compared to the state-of-the-art walking detector. In addition, mSIMPAD is scalable and applicable to real-time applications since it operates in linear time complexity.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3396250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44811359","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}
K. Nikolaidis, Stein Kristiansen, T. Plagemann, V. Goebel, K. Liestøl, M. Kankanhalli, G. Traaen, B. Overland, H. Akre, L. Aakerøy, S. Steinshamn
Sleep apnea is a common yet severely under-diagnosed sleep related disorder. Unattended sleep monitoring at home with low-cost sensors can be leveraged for condition detection, and Machine Learning offers a generalized solution for this task. However, patient characteristics, lack of sufficient training data, and other factors can imply a domain shift between training and end-user data and reduced task performance. In this work, we address this issue with the aim to achieve personalization based on the patient’s needs. We present an unsupervised domain adaptation (UDA) solution with the constraint that labeled source data are not directly available. Instead, a classifier trained on the source data is provided. Our solution iteratively labels target data sub-regions based on classifier beliefs, and trains new classifiers from the expanding dataset. Experiments with sleep monitoring datasets and various sensors show that our solution outperforms the classifier trained on the source domain, with a kappa coefficient improvement from 0.012 to 0.242. Additionally, we apply our solution to digit classification DA between three well-established datasets, to investigate its generalizability, and allow for related work comparisons. Even without direct access to the source data, it outperforms several well-established UDA methods in these datasets.
{"title":"My Health Sensor, My Classifier – Adapting a Trained Classifier to Unlabeled End-User Data","authors":"K. Nikolaidis, Stein Kristiansen, T. Plagemann, V. Goebel, K. Liestøl, M. Kankanhalli, G. Traaen, B. Overland, H. Akre, L. Aakerøy, S. Steinshamn","doi":"10.1145/3559767","DOIUrl":"https://doi.org/10.1145/3559767","url":null,"abstract":"Sleep apnea is a common yet severely under-diagnosed sleep related disorder. Unattended sleep monitoring at home with low-cost sensors can be leveraged for condition detection, and Machine Learning offers a generalized solution for this task. However, patient characteristics, lack of sufficient training data, and other factors can imply a domain shift between training and end-user data and reduced task performance. In this work, we address this issue with the aim to achieve personalization based on the patient’s needs. We present an unsupervised domain adaptation (UDA) solution with the constraint that labeled source data are not directly available. Instead, a classifier trained on the source data is provided. Our solution iteratively labels target data sub-regions based on classifier beliefs, and trains new classifiers from the expanding dataset. Experiments with sleep monitoring datasets and various sensors show that our solution outperforms the classifier trained on the source domain, with a kappa coefficient improvement from 0.012 to 0.242. Additionally, we apply our solution to digit classification DA between three well-established datasets, to investigate its generalizability, and allow for related work comparisons. Even without direct access to the source data, it outperforms several well-established UDA methods in these datasets.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44132293","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}
A. Hussein, Marc Djandji, Reem A. Mahmoud, Mohamad Dhaybi, Hazem M. Hajj
Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.
{"title":"Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures","authors":"A. Hussein, Marc Djandji, Reem A. Mahmoud, Mohamad Dhaybi, Hazem M. Hajj","doi":"10.1145/3386580","DOIUrl":"https://doi.org/10.1145/3386580","url":null,"abstract":"Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1 - 18"},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3386580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49524192","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}
Vital signs, such as respiration and heartbeat, are useful for health monitoring because such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this article, we present PhaseBeat to exploit channel state information, in particular, phase difference data to monitor breathing and heart rates with commodity WiFi devices. We provide a rigorous analysis of channel state information phase difference with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.
{"title":"On CSI-Based Vital Sign Monitoring Using Commodity WiFi","authors":"Xuyu Wang, Chao Yang, S. Mao","doi":"10.1145/3377165","DOIUrl":"https://doi.org/10.1145/3377165","url":null,"abstract":"Vital signs, such as respiration and heartbeat, are useful for health monitoring because such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this article, we present PhaseBeat to exploit channel state information, in particular, phase difference data to monitor breathing and heart rates with commodity WiFi devices. We provide a rigorous analysis of channel state information phase difference with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3377165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42391045","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}
Orpaz Goldstein, Mohammad Kachuee, Kimmo Kärkkäinen, M. Sarrafzadeh
Healthcare big data remains under-utilized due to various incompatibility issues between the domains of data analytics and healthcare. The lack of generalizable iterative feature acquisition methods under budget and machine learning models that allow reasoning with a model’s uncertainty are two examples. Meanwhile, a boost to the available data is currently under way with the rapid growth in the Internet of Things applications and personalized healthcare. For the healthcare domain to be able to adopt models that take advantage of this big data, machine learning models should be coupled with more informative, germane feature acquisition methods, consequently adding robustness to the model’s results. We introduce an approach to feature selection that is based on Bayesian learning, allowing us to report the level of uncertainty in the model, combined with false-positive and false-negative rates. In addition, measuring target-specific uncertainty lifts the restriction on feature selection being target agnostic, allowing for feature acquisition based on a target of focus. We show that acquiring features for a specific target is at least as good as deep learning feature selection methods and common linear feature selection approaches for small non-sparse datasets, and surpasses these when faced with real-world data that is larger in scale and sparseness.
{"title":"Target-Focused Feature Selection Using Uncertainty Measurements in Healthcare Data","authors":"Orpaz Goldstein, Mohammad Kachuee, Kimmo Kärkkäinen, M. Sarrafzadeh","doi":"10.1145/3383685","DOIUrl":"https://doi.org/10.1145/3383685","url":null,"abstract":"Healthcare big data remains under-utilized due to various incompatibility issues between the domains of data analytics and healthcare. The lack of generalizable iterative feature acquisition methods under budget and machine learning models that allow reasoning with a model’s uncertainty are two examples. Meanwhile, a boost to the available data is currently under way with the rapid growth in the Internet of Things applications and personalized healthcare. For the healthcare domain to be able to adopt models that take advantage of this big data, machine learning models should be coupled with more informative, germane feature acquisition methods, consequently adding robustness to the model’s results. We introduce an approach to feature selection that is based on Bayesian learning, allowing us to report the level of uncertainty in the model, combined with false-positive and false-negative rates. In addition, measuring target-specific uncertainty lifts the restriction on feature selection being target agnostic, allowing for feature acquisition based on a target of focus. We show that acquiring features for a specific target is at least as good as deep learning feature selection methods and common linear feature selection approaches for small non-sparse datasets, and surpasses these when faced with real-world data that is larger in scale and sparseness.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" ","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3383685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48790254","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}
The healthcare industry is the premier domain that has been significantly influenced by incorporation of Internet of Things (IoT) technology resulting in smart healthcare application. Inspired by the enormous potential of IoT technology, this research provides a framework for an IoT-based smart toilet system, which enables home-based determination of Urinary Infection (UI) efficaciously. The overall system comprises a four-layered architecture for monitoring and predicting infection in urine. The layers include the Urine Acquisition, Urine Analyzation, Temporal Extraction, and Temporal Prediction layers, which enable an individual to monitor his or her health on daily basis and predict UI so that precautionary measures can be taken at early stages. Moreover, probabilistic quantification of urine infection in the form of Degree of Infectiousness (DoI) and Infection Index Value (IIV) were performed for infection prediction based on a temporal Artificial Neural Network. In addition, the presence of UI is displayed to the user based on a Self-Organized Mapping technique. For validation purposes, numerous experimental simulations were performed on four individuals for 60 days. Results were compared with different state-of-the-art techniques for measuring the overall efficiency of the proposed system.
{"title":"IoT-Inspired Smart Toilet System for Home-Based Urine Infection Prediction","authors":"Munish Bhatia, Simranpreet Kaur, S. Sood","doi":"10.1145/3379506","DOIUrl":"https://doi.org/10.1145/3379506","url":null,"abstract":"The healthcare industry is the premier domain that has been significantly influenced by incorporation of Internet of Things (IoT) technology resulting in smart healthcare application. Inspired by the enormous potential of IoT technology, this research provides a framework for an IoT-based smart toilet system, which enables home-based determination of Urinary Infection (UI) efficaciously. The overall system comprises a four-layered architecture for monitoring and predicting infection in urine. The layers include the Urine Acquisition, Urine Analyzation, Temporal Extraction, and Temporal Prediction layers, which enable an individual to monitor his or her health on daily basis and predict UI so that precautionary measures can be taken at early stages. Moreover, probabilistic quantification of urine infection in the form of Degree of Infectiousness (DoI) and Infection Index Value (IIV) were performed for infection prediction based on a temporal Artificial Neural Network. In addition, the presence of UI is displayed to the user based on a Self-Organized Mapping technique. For validation purposes, numerous experimental simulations were performed on four individuals for 60 days. Results were compared with different state-of-the-art techniques for measuring the overall efficiency of the proposed system.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" ","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3379506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46008800","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}
HerrlichMarc, V. ReinschluesselAnke, WillemsMarkus, LanghorstNils, BlackDavid, DöringTanja, RiederChristian, KikinisRon, MalakaRainer
Informed by modern imaging techniques, current medical navigation systems support physicians during a variety of interventions, such as needle-based operations. During these, an abundance of inform...
在现代成像技术的指导下，当前的医疗导航系统支持医生进行各种干预，如针基手术。在这期间，大量的信息…
{"title":"Put That Needle There: Customized Flexible On-Body Thin-Film Displays for Medical Navigation","authors":"HerrlichMarc, V. ReinschluesselAnke, WillemsMarkus, LanghorstNils, BlackDavid, DöringTanja, RiederChristian, KikinisRon, MalakaRainer","doi":"10.1145/3386307","DOIUrl":"https://doi.org/10.1145/3386307","url":null,"abstract":"Informed by modern imaging techniques, current medical navigation systems support physicians during a variety of interventions, such as needle-based operations. During these, an abundance of inform...","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3386307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64026983","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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