Smart Health最新文献

{"title":"Comparing robotic and computer vision assessments of unilateral and bilateral reaching in healthy adults","authors":"Desmond Asante ,&nbsp;Shelby Ziccardi ,&nbsp;Stephen J. Guy ,&nbsp;Rachel L. Hawe","doi":"10.1016/j.smhl.2025.100615","DOIUrl":"10.1016/j.smhl.2025.100615","url":null,"abstract":"<div><div>Assessment of reaching is foundational to upper limb neurorehabilitation. Current neurorehabilitation needs have increased the demand for quantitative clinical assessments of bilateral coordination. Robotics and computer vision for motion tracking are two means to provide relevant quantitative metrics but have many differences including the dimensionality of reaching movements (planar versus three-dimensional) and data acquisition. We do not know how consistent measures of bilateral coordination performance are between these different assessments. In this study, we examined how one robotic and one computer vision method can identify differences between symmetrical and asymmetrical reaching, and the correlations in movement time, and hand lag between these two approaches. Thirty healthy young adults completed four reaching games using the Kinarm exoskeleton robot and a custom developed augmented reality assessment using computer vision.</div><div>We found that both approaches were able to detect well-established movement time and hand lag differences between symmetrical and asymmetrical reaching, with the differences between symmetrical and asymmetrical being larger with the computer vision approach. Moderate correlations were found between approaches for unilateral and symmetric reaching in both movement time and hand lags; however, no significant correlations were found between approaches for asymmetric reaching.</div><div>Our results show that reaching task performance differs between robotic and computer vision-based assessment. However, both approaches provide quantitative metrics of unilateral and bilateral reaching that are consistent with prior research. There are benefits and tradeoffs to each approach, and this study informs how clinicians and researchers can consider the methodological differences when determining which assessment method to use.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"38 ","pages":"Article 100615"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265534","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":"Erratum to “Real-time face mask position recognition system based on MobileNet model” [Smart Health 28 (2023) 100382]","authors":"Md Hafizur Rahman ,&nbsp;Mir Kanon Ara Jannat ,&nbsp;Md Shafiqul Islam ,&nbsp;Giuliano Grossi ,&nbsp;Sathya Bursic ,&nbsp;Md Aktaruzzaman","doi":"10.1016/j.smhl.2025.100619","DOIUrl":"10.1016/j.smhl.2025.100619","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"38 ","pages":"Article 100619"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690907","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":"Glucose data interpretation in pediatric diabetes using an artificial intelligence approach","authors":"Giovanni Paragliola ,&nbsp;Sara Campanella ,&nbsp;Valentino Cherubini ,&nbsp;Valentina Tiberi ,&nbsp;Paola Pierleoni ,&nbsp;Alberto Belli ,&nbsp;Antonio Iannilli ,&nbsp;Lorenzo Palma","doi":"10.1016/j.smhl.2025.100616","DOIUrl":"10.1016/j.smhl.2025.100616","url":null,"abstract":"<div><div>Semi-automatic solutions to monitor and treat diabetes have been recently developed, including insulin pumps and continuous glucose monitoring devices. Integrating computational techniques with electrical, communication, and information systems offers significant opportunities. However, no decision support systems are capable of adequately managing and analyzing the data provided by these devices. As a result, the high specificity and complexity of the information generated cannot be effectively utilized in everyday clinical practice. Therefore, this paper proposes an artificial-intelligent-based approach to identify distinct patterns within the glucose readings of pediatric diabetic patients. The objectives are twofold: first, to cluster the data employing a dimensionality reduction technique based on autoencoders, and second, to classify the data using the labels derived from the clustering phase to profile the glycemic trends. Furthermore, the blind evaluation conducted by medical professionals on the clustering results has offered crucial clinical validation to the work carried out. The results highlight the effectiveness and reliability of the proposed approach, achieving a classification performance with accuracy values up to 98%. The data reduction step was fundamental to speed up the subsequent processes while improving the metrics. The medical evaluation allowed us to improve the work by finding a correspondence between experimental results and clinical value.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"38 ","pages":"Article 100616"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332480","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":"Automatic and efficient micro univariate convolutional neural network framework for multiple neurological disorders from EEG signals","authors":"Shraddha Jain,&nbsp;Rajeev Srivastava,&nbsp;Sukomal Pal","doi":"10.1016/j.smhl.2025.100617","DOIUrl":"10.1016/j.smhl.2025.100617","url":null,"abstract":"<div><h3>Objective</h3><div>Neurological disorders affect millions globally, contributing to significant morbidity and mortality. This study proposes the μCNN framework for enhanced EEG data analysis to improve diagnosis and classification of multiple neurological disorders.</div></div><div><h3>Methods</h3><div>The μCNN framework utilizes a unique 6-channel EEG spectrogram representation to extract key features from frequency and correlation components. The model uses convolutional layers, max-pooling, and batch normalization to classify disorders such as schizophrenia, Parkinson's, Alzheimer's, epilepsy, and stroke.</div></div><div><h3>Results</h3><div>On a dataset of 15,600 EEG spectrograms, the μCNN achieved 98.42 % accuracy, 98.4 % sensitivity, and 99.61 % specificity, outperforming ResNet50 and AlexNet in classification tasks.</div></div><div><h3>Conclusions</h3><div>The μCNN framework accurately categorizes neurological disorders with superior diagnostic accuracy and system performance.</div></div><div><h3>Significance</h3><div>This work advances EEG signal processing using deep learning, offering significant improvements in real-time diagnostic accuracy for a wide range of neurological disorders.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"38 ","pages":"Article 100617"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332481","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":"An interpretable deep learning framework for predicting hospital readmissions from electronic health records","authors":"Fabio Azzalini ,&nbsp;Tommaso Dolci ,&nbsp;Marco Vagaggini","doi":"10.1016/j.smhl.2025.100581","DOIUrl":"10.1016/j.smhl.2025.100581","url":null,"abstract":"<div><div>With the increasing availability of patient data, modern medicine is shifting towards prospective healthcare. Electronic health records offer a variety of information useful for clinical patient characterization and the development of predictive models, given that similar medical histories often lead to analogous health progressions. One application is the prediction of unplanned hospital readmissions, an essential task for reducing healthcare costs and improving patient outcomes. While predictive models demonstrate strong performances especially with deep learning approaches, they are often criticized for their lack of interpretability, a critical requirement in the medical domain where incorrect predictions may have severe consequences for patient safety. In this paper, we propose a novel and interpretable deep learning framework for predicting unplanned hospital readmissions, supported by NLP findings on word embeddings and by ConvLSTM neural networks for better handling temporal data. We validate the framework on two predictive tasks for hospital readmission within 30 and 180 days, using real-world data. Additionally, we introduce and evaluate a model-dependent technique designed to enhance result interpretability for medical professionals. Our solution outperforms traditional machine learning models in prediction accuracy while simultaneously providing more interpretable results.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100581"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147537","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":"Dual attention holographic convolutional neural network based Ictal and Interictal States of automatic seizure detection using multi-channel scalp EEG with Humboldt squid optimization algorithm","authors":"Harikrishna Mulam ,&nbsp;V.P. Rameshkumaar ,&nbsp;Mrinal Bachute ,&nbsp;Elangovan Muniyandy","doi":"10.1016/j.smhl.2025.100599","DOIUrl":"10.1016/j.smhl.2025.100599","url":null,"abstract":"<div><div>Ictal-Interictal States of Automatic Seizer Detection using Multi-Channel Scalp EEG involve analyzing brain activity between seizures. It involves analyzing non-seizure EEG patters to improve accuracy in EEG systems. By examining EEG signals across multiple channels, algorithms identify patterns and abnormalities indicative of seizure task, improving early detection and intervention in epilepsy management. To overcome these problems, Dual attention holographic convolutional neural network with Humboldt Squid Optimization Algorithm (DAHCNN-HSOA) is proposed. The input data used in this comes from databases like the CHB-MIT datasets. The data was pre-processed using the Adaptive Morphological Wavelet Perona–Malik Filter Algorithm (AMWP-MFA). Then, utilizing the Synchrosqueezing-based Short-Time Fractional Fourier Transform (S-STFFT), features were recovered for the purpose of detecting interictal seizures and classification was performed using the Dual Attention Holographic Convolutional Neural Network (DAHCNN), and optimization was done using the Humboldt Squid Optimization Algorithm (HSOA) for detecting the type of Interictal States of Automatic Seizure and to find the positive (Seizure) and negative (Non-Seizure) region. The presented system is implemented in python. The proposed DAHCNN-HSOA is analysed in terms of efficiency by using 2 datasets and achieved 99.9 percent accuracy, 99.8 percent recall and improved over the current methods. It is a sign that the method is more efficient and can be improved further in the given sphere.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100599"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738986","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":"Smart implantable devices for cardiac health: A novel self-powered wireless ECG monitoring system using energy harvesting and machine learning-driven anomaly detection","authors":"Khadija Pervez ,&nbsp;Muhammad Izhar ,&nbsp;Adeel Ahmed ,&nbsp;Nazik Alturki ,&nbsp;Saima Abdullah","doi":"10.1016/j.smhl.2025.100582","DOIUrl":"10.1016/j.smhl.2025.100582","url":null,"abstract":"<div><div>Introduction of smart implantable devices is changing the face of cardiac health monitoring through continuous and real time ECG monitoring useful in early diagnosis of cardiac pathologies. This paper describes CardioHarvest-Net, a newly developed self-powered wireless ECG monitoring system that employs, physiological movements for its power in order to reduce the probability of frequent power replenishment. This self-powered capability eliminates dependency on conventional batteries, thereby offering a viable solution for continuous, long-term cardiac monitoring in real-world conditions. CardioHarvest-Net enables an enhanced machine learning (ML)-based anomaly detection model that learns and adapt to each patient's cardiac behavior to provide high sensitivity in abnormal ECG signs related to diseases like arrhythmia, myocardial infarction, and other diseases of the heart. The CardioHarvest-Net model applies CNN for feature extraction of vital signs such as ECG and uses LSTM for temporal feature extraction for accurate anomaly detection in real-world settings. Evaluation results reveal that gain scores of cardio health phenomena via CardioHarvest-Net is a detection accuracy of 97.2 % and the anomaly recall rate of 95.3 % that qualifies the proposed system as an effective and timely monitoring tool of putting up a signal and cautionary measure on possible event of cardiac occurrences. The average response time for an entire system to detect an anomaly is 10 ms, which makes the system's intervention capacity rather fast. Moreover, they use a power build-up efficiency of 78 % in otherwise low power, real-life in-vivo conditions ranging from acute circumstances to chronic conditions requiring prolonged operation. This ML model is running on an energy-efficient microcontroller suitable for wearable and implantable medical devices along with a feedback adaptation that enhances the accuracy of the predictions based on data that changes over time concerning an individual patient. The outcomes of this study further state the viability of CardioHarvest-Net to transform sustainable cardiac niche by addressing limitations into power independence and facilitating real-time tracking. This development is a breakthrough in moving towards the preventive, long-term approach to cardiac reliability enhancing our method in a manner that offers a solid framework for constant, individualized cardiac monitoring, and timely action in cases of essential occurrences in the heart.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100582"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068558","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":"Privacy-enhanced heart stroke detection using Federated Learning and Homomorphic Encryption","authors":"Vankamamidi S. Naresh ,&nbsp;Gadhiraju Tej Varma","doi":"10.1016/j.smhl.2025.100594","DOIUrl":"10.1016/j.smhl.2025.100594","url":null,"abstract":"<div><div>Heart stroke detection plays a vital role in early diagnosis and intervention leveraging machine learning (ML) models to predict stroke events from medical data. However, the use of such models often faces significant challenges related to data privacy and security, especially when sensitive health information is involved. To tackle these concerns, we present an innovative privacy-preserving approach for heart stroke detection using Federated Learning (FL) combined with Homomorphic Encryption (HE) utilizing the Cheon-Kim-Kim-Song (CKKS) scheme. FL is employed to enable edge nodes to locally train a Feed Forward Neural Network (FFNN) model on heart stroke data, without transferring any raw patient data to a central server, thus ensuring the data remains decentralized and private. During the communication between edge nodes and central server, CKKS encryption ensures that model updates remain encrypted throughout the aggregation process, allowing computations to be performed without decryption, thus enhancing privacy and security. Furthermore, the FedAvg is incorporated to aggregate model updates efficiently, ensuring robust model training while maintaining decentralized data integrity. Our model achieved an average accuracy of over 95 % in predicting heart stroke events, demonstrating the effectiveness of combining FL with an FFNN in terms of both performance and privacy. This approach allows the utilization of rich medical data for training while maintaining the security and confidentiality of sensitive health information, making it an effective option for real-world healthcare applications in contexts where privacy and security of data is of the utmost importance.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100594"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184264","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":"Classification of body mass index levels using breast thermography: A preliminary proof-of-concept analysis with convolutional neural networks","authors":"Rodrigo M. Carrillo-Larco","doi":"10.1016/j.smhl.2025.100597","DOIUrl":"10.1016/j.smhl.2025.100597","url":null,"abstract":"<div><h3>Background</h3><div>Thermal imaging has shown promise in distinguishing between obese and non-obese individuals, yet its potential in stratifying thinner body mass index (BMI) categories remains largely unexplored.</div></div><div><h3>Methods</h3><div>We utilized thermal images depicting the upper abdomen to the neck of women (aged 18–81) with benign breast pathology, each comprising anterior, oblique left, and oblique right views. Employing transfer learning and convolutional neural networks (CNN), we classified images into normal weight, overweight, and obesity categories. GradCAM activation maps identified influential areas in the images.</div></div><div><h3>Results</h3><div>84 women were included in the analysis, with a mean age of 45 years (standard deviation (SD): 12.2). The average BMI was 28.6 kg/m² (SD: 6.5), and BMI categories were evenly distributed. The overall accuracy was 87.0 %. The model performed best for the obesity category (precision: 100 %, recall: 93 %, and F1 score: 97 %). The lowest precision was observed for the normal weight category (75 %), while the overweight category had the lowest recall (67 %) and F1 score (76 %). The confusion matrix showed that misclassifications were predominantly between the normal weight and overweight categories. Activation maps showed that in the normal weight group, the sternum area was most influential; for the obesity group, regions above the armpits were emphasized; and for the overweight group, key areas included the upper abdomen, below the breasts, and upper chest.</div></div><div><h3>Conclusions</h3><div>Thermal imaging effectively differentiated between BMI categories. Further validation of thermal imaging's predictive capabilities is warranted considering the limited and women-only sample herein analyzed.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100597"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523367","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":"A non-immersive virtual reality serious game to assess distractor inhibition and spatial attention in post-stroke individuals","authors":"Gregorio Sorrentino ,&nbsp;Gauthier Everard ,&nbsp;Florence Vanhoof ,&nbsp;Thierry Lejeune ,&nbsp;Martin Gareth Edwards","doi":"10.1016/j.smhl.2025.100595","DOIUrl":"10.1016/j.smhl.2025.100595","url":null,"abstract":"<div><h3>Background</h3><div>REAsmash, a non-immersive virtual reality serious game based on Feature Integration Theory (FIT) was used to evaluate distractor inhibition and spatial attention.</div></div><div><h3>Methods</h3><div>Participants (15 post-stroke and 15 age matched healthy controls) performed the visual search REAsmash task with manipulations of high and low target-distractor salience and distractor number. Dependent variables included frequency of errors and omissions and mean correct response time.</div></div><div><h3>Results</h3><div>Post-stroke participants made more errors (χ²(1) = 19.452, p &lt; 0.001) and omissions (χ²(1) = 55.108, p &lt; 0.001), and responded slower (F(1,28) = 17.957, p &lt; 0.001, η² = 0.391) than controls. FIT significant effects showed salience (F(1,28) = 497.626, p &lt; 0.001, η² = 0.947), distractor number (F(2,56) = 24.968, p &lt; 0.001, η² = 0.471), and a salience and distractor number interaction (F(2,56) = 26.616, p &lt; 0.001, η² = 0.487), with increased distractor number slowing response time in the low salience condition. A salience-by-group interaction (F(1,28) = 7.794, p = 0.009, η² = 0.218) indicated greater post-stroke difficulties in low salience conditions. A target-hand congruency (Simon) effect was observed only in post-stroke participants (F(1,28) = 4.499, p = 0.043, η² = 0.138).</div></div><div><h3>Conclusion</h3><div>REAsmash-niVR replicated FIT results, confirming its validity for assessing distractor inhibition and spatial attention in post-stroke populations. Future research will compare individuals with and without visual neglect and explore rehabilitation applications to improve spatial attention and motor responses.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100595"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144469934","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}