Smart Health最新文献

{"title":"Secondary care for subjects with stroke: Compliance, usability and technological acceptance of the vCare platform solution","authors":"Agnese Seregni ,&nbsp;Peppino Tropea ,&nbsp;Riccardo Re ,&nbsp;Verena Biscaro ,&nbsp;Elda Judica ,&nbsp;Massimo Caprino ,&nbsp;Kai Gand ,&nbsp;Hannes Schlieter ,&nbsp;Massimo Corbo","doi":"10.1016/j.smhl.2024.100483","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100483","url":null,"abstract":"<div><p>The continuity of care of subjects with chronic Non-Communicable Diseases (NCDs) is a well-known public health problem. To address this issue, various home-based-technological solutions have been proposed to provide personalized home rehabilitation plans: however, enhancing the compliance is still a challenge. In the framework of the vCare project, an innovative technological home-based platform was developed to provide care and rehabilitation services (motor and cognitive training, e-learning service, and recommendations for additional activities) within a coaching environment in a real-life scenario.</p><p>The aim of this work was to evaluate the compliance of post stroke subjects with the solution, and the platform's usability and technological acceptance.</p><p>Patients with stroke underwent the personalized home rehabilitation plan for up to 9 weeks. Clinical status and quality of life were assessed before and after the experimental period; compliance, usability and technological acceptance at the end.</p><p>Patients experienced the vCare solution without adverse events following their clinical plan. Results were suitable: motor and cognitive training reached 66% and 95% of adherence, respectively. Usability and technological acceptance were above the limits of acceptability.</p><p>The vCare coaching system might potentially motivate and empower patients with functional disabilities to actively engage themselves in carrying out, autonomously, personalized rehabilitation activities at home.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100483"},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643625","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":"Illuminating precise stencils on surgical sites using projection-based augmented reality","authors":"Muhammad Twaha Ibrahim ,&nbsp;Aditi Majumder ,&nbsp;M. Gopi ,&nbsp;Lohrasb R. Sayadi ,&nbsp;Raj M. Vyas","doi":"10.1016/j.smhl.2024.100476","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100476","url":null,"abstract":"<div><p>In this paper we propose a system that connects surgeons to remote or local experts who provide real-time surgical guidance by illuminating salient markings or stencils (e.g. points, lines and curves) on the physical surgical site using a projector. The projection can be modified in real time by the expert using a GUI and can be seen by all in the operating room (OR) without the use of any wearables. This system overcomes the limitations of AR/VR headsets which can overlay information through a headset, but are obtrusive, not very accurate with movements, and visible only to the surgeon excluding others in the room. Overlaying information, at high precision, directly on the physical surgical site that can be seen by everyone in the OR can become an useful tool for skill transfer, expert consultation and training, especially in telemedicine.</p><p>In addition to the projector, the system comprises of a RGB-D camera (e.g. Kinect) for feedback, together designated as the PDC (<u>P</u>rojector <u>D</u>epth <u>C</u>amera) unit. The PDC is driven by a PC. The RGB-D camera provides depth information in addition to an image at video frame rates. A high resolution mesh of the surgical site is captured using the PDC unit initially. During the surgical planning, training or execution session, this digital model can be marked by appropriate incision markings on a tablet or monitor using touch based or mouse based interface, on the same local machine or after being transmitted to a remote machine. These markings are then communicated back to the PDC unit and illuminated at high precision via the projector on the surgical site in real time. If the surgical site moves during the process, the movement is tracked and updated quickly on the surgical site. Our method specifically overcomes the obtrusive, exclusive, and indirect attributes of headsets and displays while maintaining high accuracy of registration with movements.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100476"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000321/pdfft?md5=a5b29d483e4f78a016c0c96ecde0304f&pid=1-s2.0-S2352648324000321-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding reciprocity in human–robot interactions through completion of a pregiving favor","authors":"Reilly Moberg,&nbsp;Edward Downs,&nbsp;Abby Shelby,&nbsp;Arshia Khan","doi":"10.1016/j.smhl.2024.100466","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100466","url":null,"abstract":"<div><p>In today’s world, understanding how people interact with humanoid social robots is important for day-to-day interactions and design purposes. A phasic, between-subjects, psychophysiological experiment (N <span><math><mo>=</mo></math></span> 72) examined how the norm of reciprocity influenced interactions with the humanoid social robot, “Pepper”. Facial electromyography (zygomatic and corrugator) was measured to determine participant’s emotional valence during interaction. The level of reciprocity in response to a pregiving favor was measured by the number of raffle tickets purchased by participants at the robot’s request. Results suggest that the social rule of reciprocation exists within human–robot interaction. When Pepper offered a pregiving favor to a participant, that person was more likely to reciprocate via the robot’s later ticket purchase request. Contributions to theory and design of humanoid social robots are discussed, as well as avenues for future research.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100466"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140535706","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":"Multimodal speech recognition using EEG and audio signals: A novel approach for enhancing ASR systems","authors":"Anarghya Das ,&nbsp;Puru Soni ,&nbsp;Ming-Chun Huang ,&nbsp;Feng Lin ,&nbsp;Wenyao Xu","doi":"10.1016/j.smhl.2024.100477","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100477","url":null,"abstract":"<div><p>Speech recognition using EEG signals captured during covert (imagined) speech has garnered substantial interest in Brain–Computer Interface (BCI) research. While the concept holds promise, current implementations must improve performance compared to established Automatic Speech Recognition (ASR) methods using audio. An area often underestimated in previous studies is the potential of EEG utilization during overt speech. Integrating overt EEG signals with speech data by leveraging advancements in deep learning presents significant potential to enhance the efficacy of these systems. This integration proves particularly advantageous in noisy environments and for individuals with speech impairments—challenges even conventional ASR techniques struggle to address effectively. Our investigation delves into this relationship by introducing a novel multimodal model that merges EEG and speech inputs. Our model achieves a multiclass classification accuracy of 95.39%. When subjected to artificial white noise added to the input audio, our model exhibits a notable level of resilience, surpassing the capabilities of models reliant solely on single EEG or audio modalities. The validation process, leveraging the robust techniques of t-SNE and silhouette coefficient, corroborates and solidifies these advancements.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100477"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140549072","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":"ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical prediction","authors":"Chen Song,&nbsp;Xinghua Shi","doi":"10.1016/j.smhl.2024.100469","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100469","url":null,"abstract":"<div><p>The growing distribution of deep learning models to individuals’ devices on sensitive healthcare data introduces challenging privacy and security problems when computation is being operated on an untrusted server. Homomorphic encryption (HE) is one of the appropriate cryptographic techniques to provide secure machine learning computation by directly computing over encrypted data, so that allows the data owner and model owner to outsource processing of sensitive information to an untrusted server without leaking any information about the data. However, most current HE schemes only support limited arithmetic operations, which significantly hinder their applications to implement a secure deep learning algorithm, especially on the nonlinear activation function of a deep neural network. In this paper, we develop a novel HE-friendly deep neural network, named REsidue ACTivation HE (ReActHE), to implement a precise and privacy-preserving algorithm with a non-approximating HE scheme on the activation function. We consider a residue activation strategy with a scaled power activation function in a deep neural network for HE-friendly nonlinear activation. Moreover, we propose a residue activation network structure to constrain the latent space in the training process to alleviate the optimization difficulty. We comprehensively evaluate the proposed ReActHE method using various biomedical datasets and widely-used image datasets. Our results demonstrate that ReActHE outperforms other alternative solutions to secure machine learning with HE and achieves low approximation errors in classification and regression tasks.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100469"},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643624","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":"Assessing perceived stress, sleep disturbance, and fatigue among pilot and non-pilot trainees","authors":"Samuel Andres Gomez ,&nbsp;Sudip Vhaduri ,&nbsp;Mark D. Wilson ,&nbsp;Julius C. Keller","doi":"10.1016/j.smhl.2024.100472","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100472","url":null,"abstract":"<div><p>Pilots operating in a distinctive professional realm face substantial stress and fatigue from the crucial responsibility of navigating airplanes at high altitudes with inherent risks. Similarly, college-level students encounter heightened stress and fatigue while pursuing academic goals and engaging in different activities. Stress, a non-specific response to various demands, and fatigue, characterized by extreme tiredness, are prevalent health conditions experienced across a spectrum of intensity in daily life. In this study, we conduct an extensive analysis to address a fundamental question: how do stress, sleep disturbance, and fatigue experiences differ between pilots and non-pilot college students? Delving into stress and fatigue levels within these populations contributes to understanding these phenomena and their potential implications for overall well-being and performance. Building on a comprehensive analysis of the Perceived Stress Scale (PSS), Jenkins Sleep Scale (JSS), and Multidimensional Fatigue Inventory (MFI) scores, we explore variations in stress, sleep disturbance, and fatigue across multiple dimensions. Our findings indicate intriguing disparities among pilot and non-pilot cohorts. Through graphical representations and statistical tests, we reveal that non-pilot college students exhibit higher perceived stress and sleep disturbance levels. In contrast, pilots demonstrate expected higher perceived fatigue levels. Our detailed analysis of subcategories, including General Fatigue, Physical Fatigue, Reduced Activity, Reduced Motivation, and Mental Fatigue, sheds light on the complexity of these differences. Notably, pilot students experience heightened fatigue, potentially linked to the demanding nature of their tasks. In conclusion, our extended analysis contributes valuable insights into the intricate dynamics of stress, sleep disturbance, and fatigue among pilot and non-pilot college students. These findings hold implications for future research and interventions aimed at enhancing the well-being and performance of individuals in these distinct educational and professional domains.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100472"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140332939","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":"Semi-Path: An interactive semi-supervised learning framework for gigapixel pathology image analysis","authors":"Zhengfeng Lai ,&nbsp;Joohi Chauhan ,&nbsp;Dongjie Chen ,&nbsp;Brittany N. Dugger ,&nbsp;Sen-Ching Cheung ,&nbsp;Chen-Nee Chuah","doi":"10.1016/j.smhl.2024.100474","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100474","url":null,"abstract":"<div><p>The efficacy of supervised deep learning in medical image analyses, particularly in pathology, is hindered by the necessity for extensive manual annotations. Annotating images at the gigapixel level manually proves to be a highly labor-intensive and time-consuming task. Semi-supervised learning (SSL) has emerged as a promising approach that leverages unlabeled data to reduce labeling efforts. In this work, we introduce Semi-Path, a practical SSL framework enhanced with active learning (AL) for gigapixel pathology tasks. Unlike existing methods that treat SSL and AL as independent components where AL incurs significant computational complexity to SSL, we propose a deep fusion of SSL and AL into a unified framework. Our framework introduces Informative Active Annotation (IAA) that employs a SSL-AL iterative structure to effectively extract knowledge from unlabeled pathology data. This structure significantly minimizes labeling efforts and computational complexity. Then, we propose Adaptive Pseudo-Labeling (APL) to address heterogeneity in class distribution, and prediction difficulty that are often observed in real-world pathology tasks. We evaluate Semi-Path on pathology image classification and segmentation tasks over three datasets that include WSIs from breast, colorectal, and brain tissues. The experimental results demonstrate the consistent superiority of Semi-Path over state-of-the-art methods.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100474"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000308/pdfft?md5=f4f8f22379c8912b3ec2ba8e1545c8c7&pid=1-s2.0-S2352648324000308-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TinyMSI: A cost-effective handheld device for non-contact diabetic wound monitoring","authors":"Alexander Gherardi,&nbsp;Tianyu Chen,&nbsp;Huining Li,&nbsp;Jun Xia,&nbsp;Wenyao Xu","doi":"10.1016/j.smhl.2024.100468","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100468","url":null,"abstract":"<div><p>Devices characterizing diabetic foot ulcers and other wounds currently fall into two categories. Expensive clinically-oriented devices that use mature technologies such as X-ray CT and hyperspectral imaging or low-cost solutions that leverage deep learning to infer wound characterization from conventional smartphone camera images or simple surrogate markers. Mature medical-grade devices are too expensive for primary care and assisted living facilities. Low-cost solutions rely too much on indirect statistical inference to be clinically suitable. Therefore, we propose a device that leverages mature, clinically suitable optical technologies to provide a solution for these facilities. Recognizing that individual combinations of 1–2 bands of active illumination are used individually to capture pulsation, vascular, and oxygenation images. We combine all these bands into a single multispectral lighting source to create a multi-functional, reliable device for wound assessment. We selected these bands to leverage CMOS cameras near orthogonality between the RGB channels and leverage that CMOS cameras can also sense near IR light if a filter is not present, reducing overall system complexity and needed bands. For each function, the necessary lights are turned on, and the captured raw video is then fed to the corresponding sequence of image processing steps. No deep learning models are used, so large training datasets are not required. Our device is also small, lightweight, and handheld.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100468"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330591","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":"Semantic learning and attention dynamics for behavioral classification in police narratives","authors":"Dinesh Chowdary Attota,&nbsp;Abm Adnan Azmee,&nbsp;Md. Abdullah Al Hafiz Khan,&nbsp;Yong Pei,&nbsp;Dominic Thomas,&nbsp;Monica Nandan","doi":"10.1016/j.smhl.2024.100479","DOIUrl":"10.1016/j.smhl.2024.100479","url":null,"abstract":"<div><p>The proactive identification of behavioral health incidents concerns from police reports is a critical yet underexplored area. The law enforcement officers provide follow-up services to improve community life by manually analyzing and identifying generated public narrative reports after the 911 incident calls. Therefore, automatically identifying these behavioral health calls from public narrative reports helps reduce the current manual labor-intensive identification process for law enforcement officers. In this work, we introduce a novel, multi-faceted approach that combines manual expert annotations, natural language processing (NLP), and cutting-edge machine learning strategies to classify and understand these incidents within police narratives efficiently. Our proposed method retrieves relevant narrative reports utilizing domain knowledge as behavioral health cues as terms/keywords. Our approach automatically extracts different categories of behavioral health cues by utilizing the limited domain knowledge enabled behavioral health terms using a cosine similarity-based thresholding approach. The behavioral health classification model employs an automatic attention-aware feature representation of terms/keywords, categories, and narrative reports to identify behavioral health cases with an accuracy of 85%. Extensive evaluation shows that our proposed model outperforms all the state-of-the-art models by approximately 4%.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100479"},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140276832","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":"Integrating wearable sensor data and self-reported diaries for personalized affect forecasting","authors":"Zhongqi Yang ,&nbsp;Yuning Wang ,&nbsp;Ken S. Yamashita ,&nbsp;Elahe Khatibi ,&nbsp;Iman Azimi ,&nbsp;Nikil Dutt ,&nbsp;Jessica L. Borelli ,&nbsp;Amir M. Rahmani","doi":"10.1016/j.smhl.2024.100464","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100464","url":null,"abstract":"<div><p>Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These studies typically focus on objective sensory measures, often neglecting other forms of self-reported information like diaries and notes. In this paper, we propose a multimodal deep learning model for affect status forecasting. This model combines a transformer encoder with a pre-trained language model, facilitating the integrated analysis of objective metrics and self-reported diaries. To validate our model, we conduct a longitudinal study, enrolling college students and monitoring them over a year, to collect an extensive dataset including physiological, environmental, sleep, metabolic, and physical activity parameters, alongside open-ended textual diaries provided by the participants. Our results demonstrate that the proposed model achieves predictive accuracy of 82.50% for positive affect and 82.76% for negative affect, a full week in advance. The effectiveness of our model is further elevated by its explainability.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100464"},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000205/pdfft?md5=fba9945742784e6fc163d0c9ab338104&pid=1-s2.0-S2352648324000205-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308586","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}