ACM transactions on computing for healthcare最新文献

{"title":"Introduction to the Special Issue on the Wearable Technologies for Smart Health","authors":"D. Kotz, G. Xing","doi":"10.1145/3423967","DOIUrl":"https://doi.org/10.1145/3423967","url":null,"abstract":"Wearable health-tracking consumer products are gaining popularity, including smartwatches, 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 1 of a two-part collection of articles on this topic, we are pleased to share seven articles about the use of wearables for emotion sensing, physiotherapy, virtual reality, automated meal detection, a human data model, and a survey of physical-activity tracking. In the first article, “EmotionSense: An Adaptive Emotion Recognition System Based on Wearable Smart Devices”, Wang et al. propose an adaptive emotion recognition system based on smartwatches. The proposed approach first identifies user activities and employs an adaptive emotion-recognition method that extracts finegrained features from multi-mode sensory data and characterizes different emotions. This work demonstrates that wearable devices like smartwatches have made it possible to recognize physiological and behavioral patterns of humans in a convenient and non-invasive manner. In the next article, “Physiotherapy over a Distance: The Use of Wearable Technology for Video Consultations in Hospital Settings”, Aggarwal et al. report the findings of a field evaluation of a wearable technology, called SoPhy, in assessment of lower-limb movements in video consultations. The results show a number of advantages of the wearable systems like SoPhy, including helping physiotherapists in identifying subtle differences in the patient’s movements, increasing the diagnostic confidence of the physiotherapists and guiding more accurate assessment of the patients, and enhancing the overall clinician-patient communication in better understanding the therapy goals to the patients. Based on the findings, the article also presents design implications to guide further development of the video-consultation systems. Next, the article “On Shooting Stars: Comparing CAVE and HMD Immersive Virtual Reality Exergaming for Adults with Mixed Ability”, presents a study that explores the effects of two different iVR systems, the Cave Automated Virtual Environment (CAVE) and HTC Vive Head-Mounted Displ","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1 - 2"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3423967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47188211","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":"Wearable Physical Activity Tracking Systems for Older Adults—A Systematic Review","authors":"VargemidisDimitri, GerlingKathrin, SpielKatta, AbeeleVero Vanden, GeurtsLuc","doi":"10.1145/3402523","DOIUrl":"https://doi.org/10.1145/3402523","url":null,"abstract":"Physical activity (PA) positively impacts the quality of life of older adults, with technology as a promising factor in maintaining motivation. Within Computer Science and Engineering, research inv...","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":"1-37"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3402523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64028943","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":"EmotionSense","authors":"Zhu Wang, Zhiwen Yu, Bobo Zhao, Bin Guo, Chaoxiong Chen, Zhiyong Yu","doi":"10.1145/3384394","DOIUrl":"https://doi.org/10.1145/3384394","url":null,"abstract":"With the recent surge of smart wearable devices, it is possible to obtain the physiological and behavioral data of human beings in a more convenient and non-invasive manner. Based on such data, researchers have developed a variety of systems or applications to recognize and understand human behaviors, including both physical activities (e.g., gestures) and mental states (e.g., emotions). Specifically, it has been proved that different emotions can cause different changes in physiological parameters. However, other factors, such as activities, may also impact one’s physiological parameters. To accurately recognize emotions, we need not only explore the physiological data but also the behavioral data. To this end, we propose an adaptive emotion recognition system by exploring a sensor-enriched wearable smart watch. First, an activity identification method is developed to distinguish different activity scenes (e.g., sitting, walking, and running) by using the accelerometer sensor. Based on the identified activity scenes, an adaptive emotion recognition method is proposed by leveraging multi-mode sensory data (including blood volume pulse, electrodermal activity, and skin temperature). Specifically, we extract fine-grained features to characterize different emotions. Finally, the adaptive user emotion recognition model is constructed and verified by experiments. An accuracy of 74.3% for 30 participants demonstrates that the proposed system can recognize human emotions effectively.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"35 1","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83548182","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":"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}