Pub Date : 2025-10-23DOI: 10.1109/OJEMB.2025.3624591
L. Feld;S. Hellmers;L. Schell-Majoor;J. Koschate-Storm;T. Zieschang;A. Hein;B. Kollmeier
Objective: Older adults face a heightened fall risk, which can severely impact their health. Individual responses to unexpected gait perturbations (e.g., slips) are potential predictors of this risk. This study examines automatic detection of treadmill-generated gait perturbations using acceleration and angular velocity from everyday wearables. Detection is achieved using a deep convolutional long short-term memory (DeepConvLSTM) algorithm. Results: An F1 score of at least 0.68 and recall of 0.86 was retrieved for all data, i.e., data from hearing aids, smartphones at various positions and professional sensors at lumbar and sternum. Performance did not significantly change when combining data from different sensor positions or using only acceleration data. Conclusion: Results suggest that hearing aids and smartphones can monitor gait perturbations with similar performance as professional equipment, highlighting the potential of everyday wearables for continuous fall risk monitoring.
{"title":"Automatic Detection of Gait Perturbations With Everyday Wearable Technology","authors":"L. Feld;S. Hellmers;L. Schell-Majoor;J. Koschate-Storm;T. Zieschang;A. Hein;B. Kollmeier","doi":"10.1109/OJEMB.2025.3624591","DOIUrl":"10.1109/OJEMB.2025.3624591","url":null,"abstract":"<italic>Objective:</i> Older adults face a heightened fall risk, which can severely impact their health. Individual responses to unexpected gait perturbations (e.g., slips) are potential predictors of this risk. This study examines automatic detection of treadmill-generated gait perturbations using acceleration and angular velocity from everyday wearables. Detection is achieved using a deep convolutional long short-term memory (DeepConvLSTM) algorithm. <italic>Results:</i> An F1 score of at least 0.68 and recall of 0.86 was retrieved for all data, i.e., data from hearing aids, smartphones at various positions and professional sensors at lumbar and sternum. Performance did not significantly change when combining data from different sensor positions or using only acceleration data. <italic>Conclusion:</i> Results suggest that hearing aids and smartphones can monitor gait perturbations with similar performance as professional equipment, highlighting the potential of everyday wearables for continuous fall risk monitoring.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"570-575"},"PeriodicalIF":2.9,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12599889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497023","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}
Background: This study evaluates the performance of an automated method for detecting and classifying breast masses as Breast Imaging Reporting and Data System (BI-RADS) benign or biopsy-confirmed malignant using subtraction of temporally sequential mammograms. Mammograms from 100 women across two screening rounds (400 images: 2 views × 2 rounds × 100 cases) were retrospectively collected. The prior mammographic views were subtracted from the most recent ones, 98 image features were extracted from regions of interest, and were ranked using 8 feature selection methods. Results: Machine learning reduced false positives and detected masses with 97.06% accuracy and 0.92 AUC. True masses were classified as benign or malignant with 94.82% accuracy and 0.95 AUC, a significant improvement compared with state-of-the-art methods reported in the literature (0.95 vs. 0.90 AUC). Conclusions: The proposed approach demonstrates that temporal subtraction can improve diagnostic accuracy by up to 5%, supporting earlier detection of malignancies and enabling more personalized treatment strategies.
{"title":"Subtraction of Temporally Sequential Digital Mammograms: Enhancing the Detection and Classification of Malignant Masses in Breast Imaging","authors":"Kosmia Loizidou;Galateia Skouroumouni;Gabriella Savvidou;Anastasia Constantinidou;Eleni Orphanidou Vlachou;Anneza Yiallourou;Costas Pitris;Christos Nikolaou","doi":"10.1109/OJEMB.2025.3624977","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3624977","url":null,"abstract":"<italic>Background:</i> This study evaluates the performance of an automated method for detecting and classifying breast masses as Breast Imaging Reporting and Data System (BI-RADS) benign or biopsy-confirmed malignant using subtraction of temporally sequential mammograms. Mammograms from 100 women across two screening rounds (400 images: 2 views × 2 rounds × 100 cases) were retrospectively collected. The prior mammographic views were subtracted from the most recent ones, 98 image features were extracted from regions of interest, and were ranked using 8 feature selection methods. <italic>Results:</i> Machine learning reduced false positives and detected masses with 97.06% accuracy and 0.92 AUC. True masses were classified as benign or malignant with 94.82% accuracy and 0.95 AUC, a significant improvement compared with state-of-the-art methods reported in the literature (0.95 vs. 0.90 AUC). <italic>Conclusions:</i> The proposed approach demonstrates that temporal subtraction can improve diagnostic accuracy by up to 5%, supporting earlier detection of malignancies and enabling more personalized treatment strategies.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"591-597"},"PeriodicalIF":2.9,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11215985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455817","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}
Pub Date : 2025-10-02DOI: 10.1109/OJEMB.2025.3617224
Nikhil V. Divekar;Alicia Baxter;Robert D. Gregg
Goal: This work customizes and validates a task-agnostic bilateral knee exoskeleton controller for targeted assistance of primary neuromuscular deficits in highly impaired individuals. Methods: We leveraged the biomechanics-based structure of the default controller to implement specialized modifications, targeting primary deficits in a participant with post-polio syndrome (PPS) and a participant with multiple sclerosis (MS). We also developed a clinician-friendly Android interface to tune important gait parameters. Results: Customized assistance improved the participants' primary mobility deficits as identified by the clinician, decreasing five-times-sit-to-stand time from 18.9 s to 11.8 s for the PPS participant, and restoring normative knee flexion range of motion and reducing compensatory circumduction for the MS participant. The exoskeleton induced mixed effects on secondary outcomes. Conclusions: A biomechanics-based task-agnostic exoskeleton controller can be effectively customized through specialized modifications of the intuitive basis functions and interface-based tuning to provide targeted improvements in the unique mobility deficits of highly impaired individuals.
{"title":"Customizable Task-Agnostic Exoskeleton Control for Targeted Neuromuscular Assistance: Case Series","authors":"Nikhil V. Divekar;Alicia Baxter;Robert D. Gregg","doi":"10.1109/OJEMB.2025.3617224","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3617224","url":null,"abstract":"<italic>Goal:</i> This work customizes and validates a task-agnostic bilateral knee exoskeleton controller for targeted assistance of primary neuromuscular deficits in highly impaired individuals. <italic>Methods:</i> We leveraged the biomechanics-based structure of the default controller to implement specialized modifications, targeting primary deficits in a participant with post-polio syndrome (PPS) and a participant with multiple sclerosis (MS). We also developed a clinician-friendly Android interface to tune important gait parameters. <italic>Results:</i> Customized assistance improved the participants' primary mobility deficits as identified by the clinician, decreasing five-times-sit-to-stand time from 18.9 s to 11.8 s for the PPS participant, and restoring normative knee flexion range of motion and reducing compensatory circumduction for the MS participant. The exoskeleton induced mixed effects on secondary outcomes. <italic>Conclusions:</i> A biomechanics-based task-agnostic exoskeleton controller can be effectively customized through specialized modifications of the intuitive basis functions and interface-based tuning to provide targeted improvements in the unique mobility deficits of highly impaired individuals.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"564-569"},"PeriodicalIF":2.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11190070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351955","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}
Pub Date : 2025-09-29DOI: 10.1109/OJEMB.2025.3615394
David Anderson Lloyd;Andrei Dragomir;Bulent Ozpolat;Biykem Bozkurt;Yasemin Akay;Metin Akay
Goal: Cardiovascular disease is the leading cause of death in the USA. Coronary Artery Disease (CAD) in particular is responsible for over 40% of cardiovascular disease deaths. Early detection and treatment are critical in the reduction of deaths associated with CAD. Methods: Sound signatures of CAD vary for individual patients depending on where and how severe the blockage is. We propose the use of the artificial intelligence (AI, specifically the DeepSets architecture) to learn patient-specific acoustic biomarkers which distinguish heart sounds before and after percutaneous coronary intervention (PCI) in 12 human patients. Initially, Matching Pursuit was used to decompose the sound recordings into more granular representations called ‘atoms’. Then we used AI to classify whether a group of atoms from a single segment are from before or after PCI. Leveraging the model's learned latent representation, we can then identify groups of atoms which represent CAD-associated sounds within the original recording. Results: Our deep learning approach achieves a test-set classification accuracy of 88.06% using sounds from the full cardiac cycle. The same deep learning architecture achieves 71.43% accuracy using the isolated diastolic window sound segment alone. Conclusions: This preliminary study shows that individualized clusters of atoms represent distinct parts of heart sounds associated with occlusions, and that these clusters differentially change their spectral energy signature after PCI. We believe that using this approach with recordings from individual patients over many time points during disease and treatment progression will allow for a precise, non-invasive monitoring of an individual patient's condition based on unique heart sound characteristics learned using AI.
{"title":"AI-Based Detection of Coronary Artery Occlusion Using Acoustic Biomarkers Before and After Stent Placement","authors":"David Anderson Lloyd;Andrei Dragomir;Bulent Ozpolat;Biykem Bozkurt;Yasemin Akay;Metin Akay","doi":"10.1109/OJEMB.2025.3615394","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3615394","url":null,"abstract":"<italic>Goal:</i> Cardiovascular disease is the leading cause of death in the USA. Coronary Artery Disease (CAD) in particular is responsible for over 40% of cardiovascular disease deaths. Early detection and treatment are critical in the reduction of deaths associated with CAD. <italic>Methods:</i> Sound signatures of CAD vary for individual patients depending on where and how severe the blockage is. We propose the use of the artificial intelligence (AI, specifically the DeepSets architecture) to learn patient-specific acoustic biomarkers which distinguish heart sounds before and after percutaneous coronary intervention (PCI) in 12 human patients. Initially, Matching Pursuit was used to decompose the sound recordings into more granular representations called ‘atoms’. Then we used AI to classify whether a group of atoms from a single segment are from before or after PCI. Leveraging the model's learned latent representation, we can then identify groups of atoms which represent CAD-associated sounds within the original recording. <italic>Results:</i> Our deep learning approach achieves a test-set classification accuracy of 88.06% using sounds from the full cardiac cycle. The same deep learning architecture achieves 71.43% accuracy using the isolated diastolic window sound segment alone. <italic>Conclusions:</i> This preliminary study shows that individualized clusters of atoms represent distinct parts of heart sounds associated with occlusions, and that these clusters differentially change their spectral energy signature after PCI. We believe that using this approach with recordings from individual patients over many time points during disease and treatment progression will allow for a precise, non-invasive monitoring of an individual patient's condition based on unique heart sound characteristics learned using AI.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"557-563"},"PeriodicalIF":2.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315404","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}
Pub Date : 2025-09-29DOI: 10.1109/OJEMB.2025.3615395
Aliya Hasan;Mohammad Karim
Objective: Heart sound analysis is essential for cardiovascular disorder classification. Traditional auscultation and rule-based methods require manual feature engineering and clinical expertise. This work proposes a CNN-based model for automated multiclass heart sound classification. Results: Using MFCC features extracted from segmented real-world recordings, the model classifies heart sounds into murmur, extrasystole, extrahls, artifact, and normal. It achieves 98.7% training accuracy and 91% validation accuracy, with strong precision and recall for normal and murmur classes, and a weighted F1-score of 0.91. Conclusions: The results show that the proposed MFCC-CNN framework is robust, generalizable, and suitable for automated auscultation and early cardiac screening.
{"title":"Robust Heart Sound Analysis With MFCC and Light Weight Convolutional Neural Network","authors":"Aliya Hasan;Mohammad Karim","doi":"10.1109/OJEMB.2025.3615395","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3615395","url":null,"abstract":"<italic>Objective:</i> Heart sound analysis is essential for cardiovascular disorder classification. Traditional auscultation and rule-based methods require manual feature engineering and clinical expertise. This work proposes a CNN-based model for automated multiclass heart sound classification. <italic>Results:</i> Using MFCC features extracted from segmented real-world recordings, the model classifies heart sounds into murmur, extrasystole, extrahls, artifact, and normal. It achieves 98.7% training accuracy and 91% validation accuracy, with strong precision and recall for normal and murmur classes, and a weighted F1-score of 0.91. <italic>Conclusions:</i> The results show that the proposed MFCC-CNN framework is robust, generalizable, and suitable for automated auscultation and early cardiac screening.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"549-556"},"PeriodicalIF":2.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315383","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}
Pub Date : 2025-09-15DOI: 10.1109/OJEMB.2025.3610160
Zeyu Tang;Xiaodan Xing;Gang Wang;Guang Yang
Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or on sinogram reconstruction, which requires the release of raw data and complex reconstruction algorithms. Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms. The training pairs produced by our method closely resemble real data distributions (PSNR = 49.74 vs. 40.66, p $< $ 0.05). A multivariate Cox regression analysis involving thick slice CT images with lung fibrosis revealed that only the radiomics features extracted using our method demonstrated a significant correlation with mortality (HR = 1.19 and HR = 1.14, p $< $ 0.005). This paper represents the first to identify and address the challenge of generating appropriate paired training data for Deep Learning-based CT SR models, which enhances the efficacy and applicability of SR models in real-world scenarios.
{"title":"Enhancing Super-Resolution Network Efficacy in CT Imaging: Cost-Effective Simulation of Training Data","authors":"Zeyu Tang;Xiaodan Xing;Gang Wang;Guang Yang","doi":"10.1109/OJEMB.2025.3610160","DOIUrl":"10.1109/OJEMB.2025.3610160","url":null,"abstract":"Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or on sinogram reconstruction, which requires the release of raw data and complex reconstruction algorithms. Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms. The training pairs produced by our method closely resemble real data distributions (PSNR = 49.74 vs. 40.66, p <inline-formula><tex-math>$< $</tex-math></inline-formula> 0.05). A multivariate Cox regression analysis involving thick slice CT images with lung fibrosis revealed that only the radiomics features extracted using our method demonstrated a significant correlation with mortality (HR = 1.19 and HR = 1.14, p <inline-formula><tex-math>$< $</tex-math></inline-formula> 0.005). This paper represents the first to identify and address the challenge of generating appropriate paired training data for Deep Learning-based CT SR models, which enhances the efficacy and applicability of SR models in real-world scenarios.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"576-583"},"PeriodicalIF":2.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12599898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497010","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}
Pub Date : 2025-09-09DOI: 10.1109/OJEMB.2025.3607816
Ruijie Sun;Giles Hamilton-Fletcher;Sahil Faizal;Chen Feng;Todd E. Hudson;John-Ross Rizzo;Kevin C. Chan
Goal: Persons with blindness or low vision (pBLV) face challenges in completing activities of daily living (ADLs/IADLs). Semantic segmentation techniques on smartphones, like DeepLabV3+, can quickly assist in identifying key objects, but their performance across different indoor settings and lighting conditions remains unclear. Methods: Using the MIT ADE20K SceneParse150 dataset, we trained and evaluated AI models for specific indoor scenes (kitchen, bedroom, bathroom, living room) and compared them with a generic indoor model. Performance was assessed using mean accuracy and intersection-over-union metrics. Results: Scene-specific models outperformed the generic model, particularly in identifying ADL/IADL objects. Models focusing on rooms with more unique objects showed the greatest improvements (bedroom, bathroom). Scene-specific models were also more resilient to low-light conditions. Conclusions: These findings highlight how using scene-specific models can boost key performance indicators for assisting pBLV across different functional environments. We suggest that a dynamic selection of the best-performing models on mobile technologies may better facilitate ADLs/IADLs for pBLV.
{"title":"Training Indoor and Scene-Specific Semantic Segmentation Models to Assist Blind and Low Vision Users in Activities of Daily Living","authors":"Ruijie Sun;Giles Hamilton-Fletcher;Sahil Faizal;Chen Feng;Todd E. Hudson;John-Ross Rizzo;Kevin C. Chan","doi":"10.1109/OJEMB.2025.3607816","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3607816","url":null,"abstract":"<italic>Goal:</i> Persons with blindness or low vision (pBLV) face challenges in completing activities of daily living (ADLs/IADLs). Semantic segmentation techniques on smartphones, like DeepLabV3+, can quickly assist in identifying key objects, but their performance across different indoor settings and lighting conditions remains unclear. <italic>Methods:</i> Using the MIT ADE20K SceneParse150 dataset, we trained and evaluated AI models for specific indoor scenes (kitchen, bedroom, bathroom, living room) and compared them with a generic indoor model. Performance was assessed using mean accuracy and intersection-over-union metrics. <italic>Results:</i> Scene-specific models outperformed the generic model, particularly in identifying ADL/IADL objects. Models focusing on rooms with more unique objects showed the greatest improvements (bedroom, bathroom). Scene-specific models were also more resilient to low-light conditions. <italic>Conclusions:</i> These findings highlight how using scene-specific models can boost key performance indicators for assisting pBLV across different functional environments. We suggest that a dynamic selection of the best-performing models on mobile technologies may better facilitate ADLs/IADLs for pBLV.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"533-539"},"PeriodicalIF":2.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141639","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}
Pub Date : 2025-09-09DOI: 10.1109/OJEMB.2025.3607556
Ruojun Li;Samuel Chibuoyim Uche;Emmanuel Agu;Kristin Grimone;Debra S. Herman;Jane Metrik;Ana M. Abrantes;Michael D. Stein
Goal: To investigate whether machine learning analyses of smartphone sensor data can discriminate whether a subject consumed alcohol or marijuana from their gait. Methods: Using first-of-a-kind impaired gait datasets, we propose MariaGait, a novel deep learning approach to distinguish between marijuana and alcohol impairment. Subjects' time-series smartphone accelerometer and gyroscope sensor gait data are first encoded into Gramian Angular Field (GAF) images that are then classified using a tiled Convolutional Neural Network (CNN) with TICA pooling. To mitigate the insufficiency of positively labeled alcohol and marijuana instances, the tiled CNN was pre-trained on sober gait samples that were more abundant. Results: MariaGait achieved an accuracy of 94.61%, F1 score of 88.61%, and 94.33% ROC AUC score in classifying whether the subject consumed alcohol or marijuana, outperforming baseline models including Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), Multi-head CNN and Multi-head LSTM, Random Forest and Support Vector Machines (SVM)). Conclusions: Our results demonstrate that MariaGait could be a practical, non-invasive approach to determine which substance a subject is impaired by from their gait.
{"title":"Discriminating Between Marijuana and Alcohol Gait Impairments Using Tile CNN With TICA Pooling","authors":"Ruojun Li;Samuel Chibuoyim Uche;Emmanuel Agu;Kristin Grimone;Debra S. Herman;Jane Metrik;Ana M. Abrantes;Michael D. Stein","doi":"10.1109/OJEMB.2025.3607556","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3607556","url":null,"abstract":"<italic>Goal:</i> To investigate whether machine learning analyses of smartphone sensor data can discriminate whether a subject consumed alcohol or marijuana from their gait. <italic>Methods:</i> Using first-of-a-kind impaired gait datasets, we propose <italic>MariaGait</i>, a novel deep learning approach to distinguish between marijuana and alcohol impairment. Subjects' time-series smartphone accelerometer and gyroscope sensor gait data are first encoded into Gramian Angular Field (GAF) images that are then classified using a tiled Convolutional Neural Network (CNN) with TICA pooling. To mitigate the insufficiency of positively labeled alcohol and marijuana instances, the tiled CNN was pre-trained on sober gait samples that were more abundant. <italic>Results:</i> <italic>MariaGait</i> achieved an accuracy of 94.61%, F1 score of 88.61%, and 94.33% ROC AUC score in classifying whether the subject consumed alcohol or marijuana, outperforming baseline models including Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), Multi-head CNN and Multi-head LSTM, Random Forest and Support Vector Machines (SVM)). <italic>Conclusions:</i> Our results demonstrate that <italic>MariaGait</i> could be a practical, non-invasive approach to determine which substance a subject is impaired by from their gait.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"540-548"},"PeriodicalIF":2.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223696","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}
Pub Date : 2025-07-28DOI: 10.1109/OJEMB.2025.3593083
Matteo B. Lodi;Nicola Curreli;Giuseppe Mazzarella;Alessandro Fanti
Goal: Magnetic scaffolds (MagS), obtained by loading polymers with magnetic nanoparticles (MNPs) or by chemical doping of bio-ceramics, can be implanted and used as thermo-seeds for interstitial cancer therapy if exposed to radiofrequency (RF) magnetic fields. MagS have the potential to pave new therapeutic routes for the treatment of deep-seated tumors, such as bone cancers or biliary tumors. However, the studies of their fundamental RF magnetic properties and the understanding of the heat dissipation mechanism are underdeveloped. Therefore, in this work an in-depth analysis of the magnetic susceptibility spectra of several representative nanocomposites thermoseeds found in the literature is performed. Methods: A Cole-Cole model, instead of the Debye formulation, is proposed and analyzed to interpret the experimentally observed different power dissipation, due to hindered Brownian relaxation and large dipole-dipole and particle-particle interactions. To this aim, a fitting procedure based on genetic algorithm is used to derive the Cole-Cole model parameters. Results: The proposed Cole-Cole model can interpret the MNPs response when dispersed in solution and when embedded in the biomaterial. Significant differences in the equilibrium susceptibility, relaxation times and, especially, the broadening parameter are observed between the ferrofluid and MagS systems. The fitting errors are below 3%, on average. Non-linear relationships between the dipole-dipole interaction dimensionless number and the Cole-Cole parameters are found. Conclusions: The findings can foster MagS design and help planning their use for RF hyperthermia treatment, ensuring a high-quality therapy.
{"title":"Modeling the Complex Susceptibility of Magnetic Nanocomposites for Deep-Seated Tumor Hyperthermia","authors":"Matteo B. Lodi;Nicola Curreli;Giuseppe Mazzarella;Alessandro Fanti","doi":"10.1109/OJEMB.2025.3593083","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3593083","url":null,"abstract":"<italic>Goal:</i> Magnetic scaffolds (MagS), obtained by loading polymers with magnetic nanoparticles (MNPs) or by chemical doping of bio-ceramics, can be implanted and used as thermo-seeds for interstitial cancer therapy if exposed to radiofrequency (RF) magnetic fields. MagS have the potential to pave new therapeutic routes for the treatment of deep-seated tumors, such as bone cancers or biliary tumors. However, the studies of their fundamental RF magnetic properties and the understanding of the heat dissipation mechanism are underdeveloped. Therefore, in this work an in-depth analysis of the magnetic susceptibility spectra of several representative nanocomposites thermoseeds found in the literature is performed. <italic>Methods:</i> A Cole-Cole model, instead of the Debye formulation, is proposed and analyzed to interpret the experimentally observed different power dissipation, due to hindered Brownian relaxation and large dipole-dipole and particle-particle interactions. To this aim, a fitting procedure based on genetic algorithm is used to derive the Cole-Cole model parameters. <italic>Results:</i> The proposed Cole-Cole model can interpret the MNPs response when dispersed in solution and when embedded in the biomaterial. Significant differences in the equilibrium susceptibility, relaxation times and, especially, the broadening parameter are observed between the ferrofluid and MagS systems. The fitting errors are below 3%, on average. Non-linear relationships between the dipole-dipole interaction dimensionless number and the Cole-Cole parameters are found. <italic>Conclusions:</i> The findings can foster MagS design and help planning their use for RF hyperthermia treatment, ensuring a high-quality therapy.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"523-532"},"PeriodicalIF":2.9,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11097358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896779","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}
Pub Date : 2025-07-18DOI: 10.1109/OJEMB.2025.3590580
Julian Shanbhag;Sophie Fleischmann;Iris Wechsler;Heiko Gassner;Jürgen Winkler;Bjoern M. Eskofier;Anne D. Koelewijn;Sandro Wartzack;Jörg Miehling
Postural instability represents one of the cardinal symptoms of Parkinson's disease (PD). Still, internal processes leading to this instability are not fully understood. Simulations using neuromusculoskeletal human models can help understand these internal processes leading to PD-associated postural deficits. In this paper, we investigated whether reduced reactivity amplitudes resulting from impairments due to PD can explain postural instability as well as increased muscle tone as often observed in individuals with PD. To simulate reduced reactivity, we gradually decreased previously optimized gain factors within the postural control circuitry of our model performing a quiet upright standing task. After each reduction step, the model was again optimized. Simulation results were compared to experimental data collected from 31 individuals with PD and 31 age- and sex-matched healthy control participants. Analyzing our simulation results, we showed that muscle activations increased with a model's reduced reactivity, as well as joint angles' ranges of motion (ROMs). However, sway parameters such as center of pressure (COP) path lengths and COP ranges did not increase as observed in our experimental data. These results suggest that a reduced reactivity does not directly lead to increased sway parameters, but could cause increased muscle tone leading to subsequent postural control alterations. To further investigate postural stability using neuromusculoskeletal models, analyzing additional internal model parameters and tasks such as perturbed upright standing requiring comparable reaction patterns could provide promising results. By enhancing such models and deepening the understanding of internal processes of postural control, these models may be used to assess and evaluate rehabilitation interventions in the future.
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