ACM transactions on computing for healthcare最新文献_第3页

WalkingWizard - A truly wearable EEG headset for everyday use WalkingWizard - 适合日常使用的真正可佩戴脑电图耳机

ACM transactions on computing for healthcare Pub Date : 2024-02-15 DOI: 10.1145/3648106

Teck Lun Goh, L. Peh

{"title":"WalkingWizard - A truly wearable EEG headset for everyday use","authors":"Teck Lun Goh, L. Peh","doi":"10.1145/3648106","DOIUrl":"https://doi.org/10.1145/3648106","url":null,"abstract":"\u0000 Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG, and commercially available dry EEG headsets. We then tested WalkingWizard using SSVEP experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0km/hr, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly - realizing\u0000 WalkingWizard Integrated\u0000 , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: Classification of SSVEP in VR environment while walking, Real-time acquisition of emotional state of users while moving around the neighbourhood, and Understanding the effect of guided meditation for relaxation.\u0000","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"118 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776668","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}

引用次数: 0

GPU Acceleration of a Conjugate Exponential Model for Cancer Tissue Heterogeneity 肿瘤组织异质性共轭指数模型的GPU加速

ACM transactions on computing for healthcare Pub Date : 2024-01-30 DOI: 10.1145/3643710

Anik Chaudhuri, Anwoy Kumar Mohanty, Manoranjan Satpathy

{"title":"GPU Acceleration of a Conjugate Exponential Model for Cancer Tissue Heterogeneity","authors":"Anik Chaudhuri, Anwoy Kumar Mohanty, Manoranjan Satpathy","doi":"10.1145/3643710","DOIUrl":"https://doi.org/10.1145/3643710","url":null,"abstract":"Heterogeneity in the cell population of cancer tissues poses many challenges in cancer diagnosis and treatment. Studying the heterogeneity in cell populations from gene expression measurement data in the context of cancer research is a problem of paramount importance. In addition, reducing the computation time of the algorithms that deal with high volumes of data has its obvious merits. Parallelizable models using Markov chain Monte Carlo methods are typically slow. This article presents a novel, computationally efficient, and parallelizable model to analyze heterogeneity in cancer tissues using GPUs. Because our model is parallelizable, the input data size does not affect the computation time much, provided the hardware resources are not exhausted. Our model uses qPCR (quantitative polymerase chain reaction) gene expression measurements to study heterogeneity in cancer tissue. We compute the cell proportion breakup by accelerating variational methods on a GPU. We test this model on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of our algorithm with those of Markov chain Monte Carlo and expectation maximization. Our new model is computationally less complex and faster than existing Bayesian models for estimating cancer tissue heterogeneity.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"5 2","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://dl.acm.org/doi/pdf/10.1145/3643710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147904413","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}

引用次数: 0

Enhancing Robust Liver Cancer Diagnosis: A Contrastive Multi-Modality Learner with Lightweight Fusion and Effective Data Augmentation 增强肝癌诊断的鲁棒性：具有轻量级融合和有效数据增强功能的多模态对比学习器

ACM transactions on computing for healthcare Pub Date : 2023-12-30 DOI: 10.1145/3639414

Pei-Xuan Li, Hsun-Ping Hsieh, Chiang Fan Yang, Ding-You Wu, Ching-Chung Ko

引用次数: 0

Subsampled Randomized Hadamard Transformation based Ensemble Extreme Learning Machine for Human Activity Recognition 基于子采样随机哈达玛变换的集合极限学习机用于人类活动识别

ACM transactions on computing for healthcare Pub Date : 2023-11-27 DOI: 10.1145/3634813

Dipanwita Thakur, Arindam Pal

{"title":"Subsampled Randomized Hadamard Transformation based Ensemble Extreme Learning Machine for Human Activity Recognition","authors":"Dipanwita Thakur, Arindam Pal","doi":"10.1145/3634813","DOIUrl":"https://doi.org/10.1145/3634813","url":null,"abstract":"Extreme Learning Machine (ELM) is becoming a popular learning algorithm due to its diverse applications, including Human Activity Recognition (HAR). In ELM, the hidden node parameters are generated at random, and the output weights are computed analytically. However, even with a large number of hidden nodes, feature learning using ELM may not be efficient for natural signals due to its shallow architecture. Due to noisy signals of the smartphone sensors and high dimensional data, substantial feature engineering is required to obtain discriminant features and address the “curse-of-dimensionality”. In traditional ML approaches, dimensionality reduction and classification are two separate and independent tasks, increasing the system’s computational complexity. This research proposes a new ELM-based ensemble learning framework for human activity recognition to overcome this problem. The proposed architecture consists of two key parts: 1) Self-taught dimensionality reduction followed by classification. 2) they are bridged by “Subsampled Randomized Hadamard Transformation” (SRHT). Two different HAR datasets are used to establish the feasibility of the proposed framework. The experimental results clearly demonstrate the superiority of our method over the current state-of-the-art methods.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229816","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}

引用次数: 0

Application of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review 应用智能鞋垫识别日常生活活动：系统回顾

ACM transactions on computing for healthcare Pub Date : 2023-11-24 DOI: 10.1145/3633785

Luigi D’Arco, Graham McCalmont, Haiying Wang, Huiru Zheng

{"title":"Application of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review","authors":"Luigi D’Arco, Graham McCalmont, Haiying Wang, Huiru Zheng","doi":"10.1145/3633785","DOIUrl":"https://doi.org/10.1145/3633785","url":null,"abstract":"Recent years have witnessed the increasing literature on using smart insoles in health and well-being, and yet, their capability of daily living activity recognition has not been reviewed. This paper addressed this need and provided a systematic review of smart insole-based systems in the recognition of Activities of Daily Living (ADLs). The review followed the PRISMA guidelines, assessing the sensing elements used, the participants involved, the activities recognised, and the algorithms employed. The findings demonstrate the feasibility of using smart insoles for recognising ADLs, showing their high performance in recognising ambulation and physical activities involving the lower body, ranging from 70% to 99.8% of Accuracy, with 13 studies over 95%. The preferred solutions have been those including machine learning. A lack of existing publicly available datasets has been identified, and the majority of the studies were conducted in controlled environments. Furthermore, no studies assessed the impact of different sampling frequencies during data collection, and a trade-off between comfort and performance has been identified between the solutions. In conclusion, real-life applications were investigated showing the benefits of smart insoles over other solutions and placing more emphasis on the capabilities of smart insoles.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"49 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239470","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}

引用次数: 0

Combining Deep Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification 结合深度学习和信号-图像编码的多模态心理健康分类

ACM transactions on computing for healthcare Pub Date : 2023-11-03 DOI: 10.1145/3631618

Kieran Woodward, Eiman Kanjo, Athanasios Tsanas

{"title":"Combining Deep Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification","authors":"Kieran Woodward, Eiman Kanjo, Athanasios Tsanas","doi":"10.1145/3631618","DOIUrl":"https://doi.org/10.1145/3631618","url":null,"abstract":"The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring emotional trajectories over long periods of time inherits some critical limitations in relation to the size of the training data. This shortcoming may hinder the development of reliable and accurate machine learning models. To address this problem, this paper proposes a framework to tackle the limitation in performing emotional state recognition: 1) encoding time series data into coloured images; 2) leveraging pre-trained object recognition models to apply a Transfer Learning (TL) approach using the images from step 1; 3) utilising a 1D Convolutional Neural Network (CNN) to perform emotion classification from physiological data; 4) concatenating the pre-trained TL model with the 1D CNN. We demonstrate that model performance when inferring real-world wellbeing rated on a 5-point Likert scale can be enhanced using our framework, resulting in up to 98.5% accuracy, outperforming a conventional CNN by 4.5%. Subject-independent models using the same approach resulted in an average of 72.3% accuracy (SD 0.038). The proposed methodology helps improve performance and overcome problems with small training datasets.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"41 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818871","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}

引用次数: 0

A Comprehensive Picture of Factors Affecting User Willingness to Use Mobile Health Applications 影响用户使用移动健康应用程序意愿的因素的综合图片

ACM transactions on computing for healthcare Pub Date : 2023-10-10 DOI: 10.1145/3626962

Shaojing Fan, Ramesh C. Jain, Mohan S. Kankanhalli

{"title":"A Comprehensive Picture of Factors Affecting User Willingness to Use Mobile Health Applications","authors":"Shaojing Fan, Ramesh C. Jain, Mohan S. Kankanhalli","doi":"10.1145/3626962","DOIUrl":"https://doi.org/10.1145/3626962","url":null,"abstract":"Mobile health (mHealth) applications have become increasingly valuable in preventive healthcare and in reducing the burden on healthcare organizations. The aim of this paper is to investigate the factors that influence user acceptance of mHealth apps and identify the underlying structure that shapes users’ behavioral intention. An online study that employed factorial survey design with vignettes was conducted, and a total of 1,669 participants from eight countries across four continents were included in the study. Structural equation modeling was employed to quantitatively assess how various factors collectively contribute to users’ willingness to use mHealth apps. The results indicate that users’ digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information. Users’ concerns about personal privacy only had a weak impact. Furthermore, users’ demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect. Our findings have implications for app designers, healthcare practitioners, and policymakers. Efforts are needed to regulate data collection and sharing and promote digital literacy among the general population to facilitate the widespread adoption of mHealth apps.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295534","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}

引用次数: 0

Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error 用提拉米苏模型估计拍间间隔:一种误差小的新方法

ACM transactions on computing for healthcare Pub Date : 2023-10-06 DOI: 10.1145/3616020

Asiful Arefeen, Ali Akbari, Seyed Iman Mirzadeh, Roozbeh Jafari, Behrooz A. Shirazi, Hassan Ghasemzadeh

{"title":"Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error","authors":"Asiful Arefeen, Ali Akbari, Seyed Iman Mirzadeh, Roozbeh Jafari, Behrooz A. Shirazi, Hassan Ghasemzadeh","doi":"10.1145/3616020","DOIUrl":"https://doi.org/10.1145/3616020","url":null,"abstract":"Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turns, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state of the art techniques.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302162","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}

引用次数: 0

Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation. 基于结果驱动的患者匹配和风险评估专家混合的临床表型

ACM transactions on computing for healthcare Pub Date : 2023-10-01 Epub Date: 2023-09-13 DOI: 10.1145/3616021

Nathan C Hurley, Sanket S Dhruva, Nihar R Desai, Joseph R Ross, Che G Ngufor, Frederick Masoudi, Harlan M Krumholz, Bobak J Mortazavi

{"title":"Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation.","authors":"Nathan C Hurley, Sanket S Dhruva, Nihar R Desai, Joseph R Ross, Che G Ngufor, Frederick Masoudi, Harlan M Krumholz, Bobak J Mortazavi","doi":"10.1145/3616021","DOIUrl":"10.1145/3616021","url":null,"abstract":"<p><p>Observational medical data present unique opportunities for analysis of medical outcomes and treatment decision making. However, because these datasets do not contain the strict pairing of randomized control trials, matching techniques are to draw comparisons among patients. A key limitation to such techniques is verification that the variables used to model treatment decision making are also relevant in identifying the risk of major adverse events. This article explores a deep mixture of experts approach to jointly learn how to match patients and model the risk of major adverse events in patients. Although trained with information regarding treatment and outcomes, after training, the proposed model is decomposable into a network that clusters patients into phenotypes from information available before treatment. This model is validated on a dataset of patients with acute myocardial infarction complicated by cardiogenic shock. The mixture of experts approach can predict the outcome of mortality with an area under the receiver operating characteristic curve of 0.85 ± 0.01 while jointly discovering five potential phenotypes of interest. The technique and interpretation allow for identifying clinically relevant phenotypes that may be used both for outcomes modeling as well as potentially evaluating individualized treatment effects.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46461728","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}

引用次数: 0

Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach 质量保证和成本效益的人口健康分析:一种深度主动学习方法

ACM transactions on computing for healthcare Pub Date : 2023-08-25 DOI: 10.1145/3617179

Long Chen, Jiangtao Wang, P. Thakuriah

引用次数: 0