The Internet-of-Medical Things (IoMT) paradigm paves the foundations for an intelligent and reliable per-sonalized precision medicine. This embedded computing paradigm aims to offer accurate multiscale medical monitoring through smart sensing, advanced analytics, and enabling continuous and rigorous medical diagno-sis, and on-the-fly communication with medical experts. It leverages mathematical and physical modeling of human anatomy and physiology to provide hyperspectral and hyperdimensional processing and restores health through precise patient-specific actuation. Along these lines of providing advanced analytics for early detection and injury of falls, in “Pervasive Pose Estimation for Fall Detection”, Luo et al. proposed a pervasive pose estimation strategy for fall detection (P2Est) on a mobile portable device (e.g., smartphone) capable to quantify changes in tilt angle and height of the human body. To quantify the tilt measurement, the P2Est exploits the pointing of the mobile device to associate the device coordinate system with the world coordinate system. To gauge the height changes, the P2Est considers that the person’s height remains relatively unchanged while walking to calibrate the pressure difference between the device and the floor. Luo et al. implemented the P2Est strategy and tested it in various environments demonstrating that it can track the body orientation irrespective of which pocket the phone is placed in. The authors also report that P2Est strategy exploits the phone’s barometer to detect falls in various environments with decimeter-level accuracy.
{"title":"Introduction to the Special Issue on Internet-of-Medical-Things","authors":"P. Bogdan, R. Grosu, Insup Lee","doi":"10.1145/3547656","DOIUrl":"https://doi.org/10.1145/3547656","url":null,"abstract":"The Internet-of-Medical Things (IoMT) paradigm paves the foundations for an intelligent and reliable per-sonalized precision medicine. This embedded computing paradigm aims to offer accurate multiscale medical monitoring through smart sensing, advanced analytics, and enabling continuous and rigorous medical diagno-sis, and on-the-fly communication with medical experts. It leverages mathematical and physical modeling of human anatomy and physiology to provide hyperspectral and hyperdimensional processing and restores health through precise patient-specific actuation. Along these lines of providing advanced analytics for early detection and injury of falls, in “Pervasive Pose Estimation for Fall Detection”, Luo et al. proposed a pervasive pose estimation strategy for fall detection (P2Est) on a mobile portable device (e.g., smartphone) capable to quantify changes in tilt angle and height of the human body. To quantify the tilt measurement, the P2Est exploits the pointing of the mobile device to associate the device coordinate system with the world coordinate system. To gauge the height changes, the P2Est considers that the person’s height remains relatively unchanged while walking to calibrate the pressure difference between the device and the floor. Luo et al. implemented the P2Est strategy and tested it in various environments demonstrating that it can track the body orientation irrespective of which pocket the phone is placed in. The authors also report that P2Est strategy exploits the phone’s barometer to detect falls in various environments with decimeter-level accuracy.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124923283","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}
Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.
{"title":"Automatic Extraction of Nested Entities in Clinical Referrals in Spanish","authors":"P. Baez, Felipe Bravo-Marquez","doi":"10.1145/3498324","DOIUrl":"https://doi.org/10.1145/3498324","url":null,"abstract":"Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129131909","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}
Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a Pervasive Pose Estimation scheme for fall detection (P ( ^2 ) Est), which measures changes in tilt angle and height of the human body. For the tilt measurement, P ( ^2 ) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P ( ^2 ) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P ( ^2 ) Est in various situations and environments. Our extensive experimental results have demonstrated that P ( ^2 ) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy.
{"title":"Pervasive Pose Estimation for Fall Detection","authors":"Jia-qin Luo, Ruiyu Bai, Suining He, K. Shin","doi":"10.1145/3478027","DOIUrl":"https://doi.org/10.1145/3478027","url":null,"abstract":"Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a Pervasive Pose Estimation scheme for fall detection (P ( ^2 ) Est), which measures changes in tilt angle and height of the human body. For the tilt measurement, P ( ^2 ) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P ( ^2 ) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P ( ^2 ) Est in various situations and environments. Our extensive experimental results have demonstrated that P ( ^2 ) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124777280","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}
Georgios Katsimpras, F. Aisopos, P. Garrard, M. Vidal, G. Paliouras
Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.
{"title":"Improving Early Prognosis of Dementia Using Machine Learning Methods","authors":"Georgios Katsimpras, F. Aisopos, P. Garrard, M. Vidal, G. Paliouras","doi":"10.1145/3502433","DOIUrl":"https://doi.org/10.1145/3502433","url":null,"abstract":"Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123613704","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}
Sagnik Ghosal, Debanjan Das, Venkanna Udutalapally, P. Wasnik
The paper presents a novel, self-sufficient, Internet of Medical Things-based model called iNAP to address the shortcomings of anemia and polycythemia detection. The proposed model captures eye and fingernail images using a smartphone camera and automatically extracts the conjunctiva and fingernails as the regions of interest. A novel algorithm extracts the dominant color by analyzing color spectroscopy of the extracted portions and accurately predicts blood hemoglobin level. A less than 11.5 gdL ( ^{-1} ) value is categorized as anemia while a greater than 16.5 gdL ( ^{-1} ) value as polycythemia. The model incorporates machine learning and image processing techniques allowing easy smartphone implementation. The model predicts blood hemoglobin to an accuracy of ( pm ) 0.33 gdL ( ^{-1} ) , a bias of 0.2 gdL ( ^{-1} ) , and a sensitivity of 90 ( % ) compared to clinically tested results on 99 participants. Furthermore, a novel brightness adjustment algorithm is developed, allowing robustness to a wide illumination range and the type of device used. The proposed IoMT framework allows virtual consultations between physicians and patients, as well as provides overall public health information. The model thereby establishes itself as an authentic and acceptable replacement for invasive and clinically-based hemoglobin tests by leveraging the feature of self-anemia and polycythemia diagnosis.
{"title":"iNAP: A Hybrid Approach for NonInvasive Anemia-Polycythemia Detection in the IoMT","authors":"Sagnik Ghosal, Debanjan Das, Venkanna Udutalapally, P. Wasnik","doi":"10.1145/3503466","DOIUrl":"https://doi.org/10.1145/3503466","url":null,"abstract":"The paper presents a novel, self-sufficient, Internet of Medical Things-based model called iNAP to address the shortcomings of anemia and polycythemia detection. The proposed model captures eye and fingernail images using a smartphone camera and automatically extracts the conjunctiva and fingernails as the regions of interest. A novel algorithm extracts the dominant color by analyzing color spectroscopy of the extracted portions and accurately predicts blood hemoglobin level. A less than 11.5 gdL ( ^{-1} ) value is categorized as anemia while a greater than 16.5 gdL ( ^{-1} ) value as polycythemia. The model incorporates machine learning and image processing techniques allowing easy smartphone implementation. The model predicts blood hemoglobin to an accuracy of ( pm ) 0.33 gdL ( ^{-1} ) , a bias of 0.2 gdL ( ^{-1} ) , and a sensitivity of 90 ( % ) compared to clinically tested results on 99 participants. Furthermore, a novel brightness adjustment algorithm is developed, allowing robustness to a wide illumination range and the type of device used. The proposed IoMT framework allows virtual consultations between physicians and patients, as well as provides overall public health information. The model thereby establishes itself as an authentic and acceptable replacement for invasive and clinically-based hemoglobin tests by leveraging the feature of self-anemia and polycythemia diagnosis.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115372851","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}
Jonathon Fagert, Amelie Bonde, Sruti Srinidhi, Sarah Hamilton, Pei Zhang, Hae Young Noh
We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across four different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents an 8.8X and 10.2X reduction in error over two different baseline approaches.
{"title":"Clean Vibes: Hand Washing Monitoring Using Structural Vibration Sensing","authors":"Jonathon Fagert, Amelie Bonde, Sruti Srinidhi, Sarah Hamilton, Pei Zhang, Hae Young Noh","doi":"10.1145/3511890","DOIUrl":"https://doi.org/10.1145/3511890","url":null,"abstract":"We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across four different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents an 8.8X and 10.2X reduction in error over two different baseline approaches.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128691","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}
M. J. Amon, Stephen M. Mattingly, Aaron Necaise, Gloria Mark, N. Chawla, Anindya Dey, Sidney K. D’Mello
Although some research highlights the benefits of behavioral routines for individual functioning, other research indicates that routines can reflect an individual's inflexibility and lower well-being. Given conflicting accounts on the benefits of routine, research is needed to examine how routineness versus flexibility in health-related behaviors correspond to personality traits, health, and occupational outcomes. We adopt a nonlinear dynamical systems approach to understanding routine using automatically sensed health-related behaviors collected from 483 information workers over a roughly two-month period. We utilized multidimensional recurrence quantification analysis to derive a measure of health regularity (routineness) from measures of daily step count, sleep duration, and heart rate variability (which relates to stress). Participants also completed measures of personality, health, and job performance at the start of the study and for two months via Ecological Momentary Assessments. Greater regularity was associated with higher neuroticism, lower agreeableness, and greater interpersonal and organizational deviance. Importantly, these results were independent of overall levels of each health indicator in addition to demographics. It is often believed that routine is desirable, but the results suggest that associations with routineness are more nuanced, and wearable sensors can provide insights into beneficial health behaviors.
{"title":"Flexibility Versus Routineness in Multimodal Health Indicators: A Sensor-based Longitudinal in Situ Study of Information Workers","authors":"M. J. Amon, Stephen M. Mattingly, Aaron Necaise, Gloria Mark, N. Chawla, Anindya Dey, Sidney K. D’Mello","doi":"10.1145/3514259","DOIUrl":"https://doi.org/10.1145/3514259","url":null,"abstract":"Although some research highlights the benefits of behavioral routines for individual functioning, other research indicates that routines can reflect an individual's inflexibility and lower well-being. Given conflicting accounts on the benefits of routine, research is needed to examine how routineness versus flexibility in health-related behaviors correspond to personality traits, health, and occupational outcomes. We adopt a nonlinear dynamical systems approach to understanding routine using automatically sensed health-related behaviors collected from 483 information workers over a roughly two-month period. We utilized multidimensional recurrence quantification analysis to derive a measure of health regularity (routineness) from measures of daily step count, sleep duration, and heart rate variability (which relates to stress). Participants also completed measures of personality, health, and job performance at the start of the study and for two months via Ecological Momentary Assessments. Greater regularity was associated with higher neuroticism, lower agreeableness, and greater interpersonal and organizational deviance. Importantly, these results were independent of overall levels of each health indicator in addition to demographics. It is often believed that routine is desirable, but the results suggest that associations with routineness are more nuanced, and wearable sensors can provide insights into beneficial health behaviors.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120944731","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}
Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.
{"title":"Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future","authors":"Maryam Pourebadi, L. Riek","doi":"10.1145/3483598","DOIUrl":"https://doi.org/10.1145/3483598","url":null,"abstract":"Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121338207","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}
Z. Hussain, Quan Z. Sheng, W. Zhang, Jorge Ortiz, Seyedamin Pouriyeh
Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.
{"title":"Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review","authors":"Z. Hussain, Quan Z. Sheng, W. Zhang, Jorge Ortiz, Seyedamin Pouriyeh","doi":"10.1145/3491245","DOIUrl":"https://doi.org/10.1145/3491245","url":null,"abstract":"Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701948","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}
Virtual reality, augmented reality, and mixed reality (VR/AR/MR) as information and communication technologies have been recognised and implemented in healthcare in recent years. One of the popular application ways is games, due to the potential benefits of providing an engaging and immersive experience in a virtual environment. This study presents a systematic literature review that evaluates the state-of-the-art on VR/AR/MR game applications in healthcare by collecting and analysing related journal and conference papers published from 2014 through to the first half of 2020. After retrieving more than 3,000 papers from six databases, 88 articles, from both computer science and medicine, were selected and analysed in the review. The articles are classified and summarised based on their (1) publication information, (2) design, implementation, and evaluation, and (3) application. The presented review is beneficial for both researchers and developers interested in exploring current research and future trends in VR/AR/MR in healthcare.
{"title":"A Systematic Literature Review of Virtual, Augmented, and Mixed Reality Game Applications in Healthcare","authors":"Yu Fu, Yan Hu, V. Sundstedt","doi":"10.1145/3472303","DOIUrl":"https://doi.org/10.1145/3472303","url":null,"abstract":"Virtual reality, augmented reality, and mixed reality (VR/AR/MR) as information and communication technologies have been recognised and implemented in healthcare in recent years. One of the popular application ways is games, due to the potential benefits of providing an engaging and immersive experience in a virtual environment. This study presents a systematic literature review that evaluates the state-of-the-art on VR/AR/MR game applications in healthcare by collecting and analysing related journal and conference papers published from 2014 through to the first half of 2020. After retrieving more than 3,000 papers from six databases, 88 articles, from both computer science and medicine, were selected and analysed in the review. The articles are classified and summarised based on their (1) publication information, (2) design, implementation, and evaluation, and (3) application. The presented review is beneficial for both researchers and developers interested in exploring current research and future trends in VR/AR/MR in healthcare.","PeriodicalId":288903,"journal":{"name":"ACM Transactions on Computing for Healthcare (HEALTH)","volume":"1981 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132703051","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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