ACM transactions on computing for healthcare最新文献

{"title":"Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications","authors":"Devender Kumar, S. Jeuris, J. Bardram, N. Dragoni","doi":"10.1145/3422158","DOIUrl":"https://doi.org/10.1145/3422158","url":null,"abstract":"With the widespread use of smartphones and wearable health sensors, a plethora of mobile health (mHealth) applications to track well-being, run human behavioral studies, and clinical trials have emerged in recent years. However, the design, development, and deployment of mHealth applications is challenging in many ways. To address these challenges, several generic mobile sensing frameworks have been researched in the past decade. Such frameworks assist developers and researchers in reducing the complexity, time, and cost required to build and deploy health-sensing applications. The main goal of this article is to provide the reader with an overview of the state-of-the-art of health-focused generic mobile and wearable sensing frameworks. This review gives a detailed analysis of functional and non-functional features of existing frameworks, the health studies they were used in, and the stakeholders they support. Additionally, we also analyze the historical evolution, uptake, and maintenance after the initial release. Based on this analysis, we suggest new features and opportunities for future generic mHealth sensing frameworks.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3422158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47998709","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":"Transfer Learning for Human Activity Recognition Using Representational Analysis of Neural Networks","authors":"Sizhe An, Ganapati Bhat, S. Gumussoy, Ümit Y. Ogras","doi":"10.1145/3563948","DOIUrl":"https://doi.org/10.1145/3563948","url":null,"abstract":"Human activity recognition (HAR) has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the hardware platform reveal that the power and energy consumption decreased by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch. Our code is released for reproducibility.1","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 21"},"PeriodicalIF":0.0,"publicationDate":"2020-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49468295","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":"Chronic Pain Protective Behavior Detection with Deep Learning","authors":"Chongyang Wang, Temitayo A. Olugbade, Akhil Mathur, A. Williams, N. Lane, N. Bianchi-Berthouze","doi":"10.1145/3449068","DOIUrl":"https://doi.org/10.1145/3449068","url":null,"abstract":"In chronic pain rehabilitation, physiotherapists adapt physical activity to patients’ performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this article, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modeled per activity type, performance achieves a mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts’ rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3449068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43298593","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","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}