ACM transactions on computing for healthcare最新文献

{"title":"Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home","authors":"Stein Kristiansen, K. Nikolaidis, T. Plagemann, V. Goebel, G. Traaen, B. Øverland, L. Aakerøy, T. Hunt, J. P. Loennechen, S. Steinshamn, C. Bendz, O. Anfinsen, L. Gullestad, H. Akre","doi":"10.1145/3433987","DOIUrl":"https://doi.org/10.1145/3433987","url":null,"abstract":"Sleep apnea is a common and strongly under-diagnosed severe sleep-related respiratory disorder with periods of disrupted or reduced breathing during sleep. To diagnose sleep apnea, sleep data are collected with either polysomnography or polygraphy and scored by a sleep expert. We investigate in this work the use of supervised machine learning to automate the analysis of polygraphy data from the A3 study containing more than 7,400 hours of sleep monitoring data from 579 patients. We conduct a systematic comparative study of classification performance and resource use with different combinations of 27 classifiers and four sleep signals. The classifiers achieve up to 0.8941 accuracy (kappa: 0.7877) when using all four signal types simultaneously and up to 0.8543 accuracy (kappa: 0.7080) with only one signal, i.e., oxygen saturation. Methods based on deep learning outperform other methods by a large margin. All deep learning methods achieve nearly the same maximum classification performance even when they have very different architectures and sizes. When jointly accounting for classification performance, resource consumption and the ability to achieve with less training data high classification performance, we find that convolutional neural networks substantially outperform the other classifiers.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3433987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43560441","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}

{"title":"Introduction to the Special Issue on the Wearable Technologies for Smart Health, Part 2","authors":"D. Kotz, G. Xing","doi":"10.1145/3442350","DOIUrl":"https://doi.org/10.1145/3442350","url":null,"abstract":"Wearable health-tracking consumer products are gaining popularity, including smart watches, fitness trackers, smart clothing, and head-mounted devices. These wearable devices promise new opportunities for the study of health-related behavior, for tracking of chronic conditions, and for innovative interventions in support of health and wellness. Next-generation wearable technologies have the potential to transform today’s hospitalcentered healthcare practices into proactive, individualized care. Although it seems new technologies enter the marketplace every week, there is still a great need for research on the development of sensors, sensor-data analytics, wearable interaction modalities, and more. In this special issue, we sought to assemble a set of articles addressing novel computational research related to any aspect of the design or use of wearables in medicine and health, including wearable hardware design, AI and data analytics algorithms, human-device interaction, security/privacy, and novel applications. Here, in Part 2 of a two-part collection of articles on this topic, we are pleased to share four articles about the use of wearables for skill assessment, activity recognition, mood recognition, and deep learning. In the first article, Generalized and Efficient Skill Assessment from IMU Data with Applications in Gymnastics and Medical Training, Khan et al. propose a new framework for skill assessment that generalizes across application domains and can be deployed for different near-real-time applications. The effectiveness and efficiency of the proposed approach is validated in gymnastics and surgical skill training of medical students. In the next article, Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring, Jourdan et al. propose a framework that uses machine learning to recognize the user activity, in the context of personal healthcare monitoring, while limiting the risk of users’ re-identification from biometric patterns that characterize an individual. Their solution trades off privacy and utility with a slight decrease of utility (9% drop in accuracy) against a large increase of privacy. Next, the article Perception Clusters: Automated Mood Recognition using a Novel Cluster-driven Modelling System proposes a mood-recognition system that groups individuals in “perception clusters” based on their physiological signals. This method can provide inference results that are more accurate than generalized models, without the need for the extensive training data necessary to build personalized models. In this regard, the approach is a compromise between generalized and personalized models for automated mood recognition (AMR). Finally, in an article about the Ensemble Deep Learning on Wearables Using Small Datasets, Ngu et al. describe an in-depth experimental study of Ensemble Deep Learning techniques on small time-series datasets generated by wearable devices, which is motivated by the fact that there","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 2"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3442350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46137824","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}

{"title":"Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living","authors":"A. Akbari, Reese Grimsley, R. Jafari","doi":"10.1145/3428664","DOIUrl":"https://doi.org/10.1145/3428664","url":null,"abstract":"Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 22"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3428664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45105424","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}

{"title":"A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.","authors":"Nathan C Hurley, Erica S Spatz, Harlan M Krumholz, Roozbeh Jafari, Bobak J Mortazavi","doi":"10.1145/3417958","DOIUrl":"10.1145/3417958","url":null,"abstract":"<p><p>Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320445/pdf/nihms-1670305.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39274866","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}

{"title":"Ensemble Deep Learning on Wearables Using Small Datasets","authors":"Taylor R. Mauldin, A. Ngu, V. Metsis, Marc E. Canby","doi":"10.1145/3428666","DOIUrl":"https://doi.org/10.1145/3428666","url":null,"abstract":"This article presents an in-depth experimental study of Ensemble Deep Learning techniques on small datasets for the analysis of time-series data generated by wearable devices. Deep Learning networks generally require large datasets for training. In some health care applications, such as the real-time smartwatch-based fall detection, there are no publicly available, large, annotated datasets that can be used for training, due to the nature of the problem (i.e., a fall is not a common event). We conducted a series of offline experiments using two different datasets of simulated falls for training various ensemble models. Our offline experimental results show that an ensemble of Recurrent Neural Network (RNN) models, combined by the stacking ensemble technique, outperforms a single RNN model trained on the same data samples. Nonetheless, fall detection models trained on simulated falls and activities of daily living performed by test subjects in a controlled environment, suffer from low precision due to high false-positive rates. In this work, through a set of real-world experiments, we demonstrate that the low precision can be mitigated via the collection of false-positive feedback by the end-users. The final Ensemble RNN model, after re-training with real-world user archived data and feedback, achieved a significantly higher precision without reducing much of the recall in a real-world setting.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 30"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3428666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45821248","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}

{"title":"Perception Clusters","authors":"Aftab Khan, Alexandros Zenonos, G. Kalogridis, Yaowei Wang, Stefanos Vatsikas, M. Sooriyabandara","doi":"10.1145/3422819","DOIUrl":"https://doi.org/10.1145/3422819","url":null,"abstract":"Automated mood recognition has been studied in recent times with great emphasis on stress in particular. Other affective states are also of great importance, as studying them can help in understanding human behaviours in more detail. Most of the studies conducted in the realisation of an automated system that is capable of recognising human moods have established that mood is personal—that is, mood perception differs amongst individuals. Previous machine learning--based frameworks confirm this hypothesis, with personalised models almost always outperforming the generalised methods. In this article, we propose a novel system for grouping individuals in what we refer to as “perception clusters” based on their physiological signals. We evaluate perception clusters with a trial of nine users in a work environment, recording physiological and activity data for at least 10 days. Our results reveal no significant difference in performance with respect to a personalised approach and that our method performs equally better against traditional generalised methods. Such an approach significantly reduces computational requirements that are otherwise necessary for personalised approaches requiring individual models developed separately for each user. Further, perception clusters manifest a direction towards semi-supervised affective modelling in which individual perceptions are inferred from the data.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 16"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3422819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44048630","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}

{"title":"Creating and Evaluating Chatbots as Eligibility Assistants for Clinical Trials","authors":"C. Chuan, Susan Morgan","doi":"10.1145/3403575","DOIUrl":"https://doi.org/10.1145/3403575","url":null,"abstract":"Clinical trials are important tools to improve knowledge about the effectiveness of new treatments for all diseases, including cancers. However, studies show that fewer than 5% of cancer patients are enrolled in any type of research study or clinical trial. Although there is a wide variety of reasons for the low participation rate, we address this issue by designing a chatbot to help users determine their eligibility via interactive, two-way communication. The chatbot is supported by a user-centered classifier that uses an active deep learning approach to separate complex eligibility criteria into questions that can be easily answered by users and information that requires verification by their doctors. We collected all the available clinical trial eligibility criteria from the National Cancer Institute's website to evaluate the chatbot and the classifier. Experimental results show that the active deep learning classifier outperforms the baseline k-nearest neighbor method. In addition, an in-person experiment was conducted to evaluate the effectiveness of the chatbot. The results indicate that the participants who used the chatbot achieved better understanding about eligibility than those who used only the website. Furthermore, interfaces with chatbots were rated significantly better in terms of perceived usability, interactivity, and dialogue.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3403575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47567756","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}

{"title":"Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring","authors":"T. Jourdan, A. Boutet, A. Bahi, Carole Frindel","doi":"10.1145/3416947","DOIUrl":"https://doi.org/10.1145/3416947","url":null,"abstract":"The increasing popularity of wearable consumer products can play a significant role in the healthcare sector. The recognition of human activities from IoT is an important building block in this context. While the analysis of the generated datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this article, we propose a framework that relies on machine learning to efficiently recognise the user activity, useful for personal healthcare monitoring, while limiting the risk of users re-identification from biometric patterns characterizing each individual. To achieve that, we show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. We then design a novel protection mechanism processing the raw signal on the user’s smartphone to select relevant features for activity recognition and normalise features sensitive to re-identification. These unlinkable features are then transferred to the application server. We extensively evaluate our framework with reference datasets: Results show an accurate activity recognition (87%) while limiting the re-identification rate (33%). This represents a slight decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 22"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3416947","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41965873","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}

{"title":"Designing Visual Markers for Continuous Artificial Intelligence Support","authors":"Niels van Berkel, O. Ahmad, D. Stoyanov, L. Lovat, A. Blandford","doi":"10.1145/3422156","DOIUrl":"https://doi.org/10.1145/3422156","url":null,"abstract":"Colonoscopy, the visual inspection of the large bowel using an endoscope, offers protection against colorectal cancer by allowing for the detection and removal of pre-cancerous polyps. The literature on polyp detection shows widely varying miss rates among clinicians, with averages ranging around 22%--27%. While recent work has considered the use of AI support systems for polyp detection, how to visualise and integrate these systems into clinical practice is an open question. In this work, we explore the design of visual markers as used in an AI support system for colonoscopy. Supported by the gastroenterologists in our team, we designed seven unique visual markers and rendered them on real-life patient video footage. Through an online survey targeting relevant clinical staff (N = 36), we evaluated these designs and obtained initial insights and understanding into the way in which clinical staff envision AI to integrate in their daily work-environment. Our results provide concrete recommendations for the future deployment of AI support systems in continuous, adaptive scenarios.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" ","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3422156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44225886","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}

{"title":"Generalized and Efficient Skill Assessment from IMU Data with Applications in Gymnastics and Medical Training","authors":"Aftab Khan, Sebastian Mellor, R. King, Balazs Janko, W. Harwin, R. Sherratt, I. Craddock, T. Plötz","doi":"10.1145/3422168","DOIUrl":"https://doi.org/10.1145/3422168","url":null,"abstract":"Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 21"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3422168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46052762","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}