Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献

{"title":"Impact of multidirectional vibratory feedback on posture control during standing and weight lifting: a pilot study.","authors":"Hugo M Martins, Matheus G Nogueira, Pedro Parik-Americano, Rafael T Moura, Arturo Forner-Cordero, Cristina P Camargo","doi":"10.1109/EMBC53108.2024.10782169","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782169","url":null,"abstract":"<p><p>In this pilot study, we investigated the influence of vibratory feedback (VF) on postural control (PC) during frontal weight elevation and standing. We developed a multidirectional VF belt to provide feedback to the user on trunk inclination to correct the center of pressure (CoP) sway. CoP data were measured with a force plate, and user experience was collected through assessed questionnaires. Our findings suggest that VF contributes to a rapid return of the CoP to the Base of Support (BoS) during weight lifting(WL). However, VF may pose challenges during open-eyed conditions or with the addition of a compliant platform, potentially overwhelming users with proprioceptive stimuli and requiring increased attention. Although VF aids in postural correction near the support base and its periphery, individual factors such as user attention and sensitivity influence VF perception. Further studies with larger sample sizes are needed to validate these findings and explore the efficacy of VF in diverse postural control scenarios.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559616","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":"Improving Bioimpedance-based Tissue Identification with Frequency Response Similarity Metrics.","authors":"Jacob Search, Sabino Zani, Brian P Mann","doi":"10.1109/EMBC53108.2024.10782337","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782337","url":null,"abstract":"<p><p>Tissue identification is essential for surgeons to properly perform procedures and make informed decisions to minimize potential harm to patients. Minimally invasive surgery (MIS) offers enhanced patient safety and outcomes at the cost of lost information due to restricted vision and loss of touch, among other factors. This makes it more difficult to quickly and consistently identify tissues correctly. Bioimpedance spectroscopy (BIS) offers the potential to identify tissues using rapid measurements that leverage differences in electrical properties between tissues. However, using BIS to differentiate large sets of tissues in a singular anatomical area, such as the gastrointestinal (GI) tract, has remained a significant challenge because of the overlap of similar tissues' responses and variability between measurements. This work proposes the application of frequency response function (FRF) similarity metrics as a signal processing technique to extract new features from BIS measurements on porcine tissues. These features are then used as inputs to machine learning (ML) models that are trained on an ex vivo dataset for identification of eight different in vivo porcine abdominal tissues. The ML models using similarity metric inputs performed on par or better than models using raw measurement inputs, except for the support vector machine (SVM) models. A neural network (NN) model using a similarity metric input performed best by achieving a mean accuracy of 70.3% and F-measure of 0.716. More importantly, the similarity metrics enhanced the ability of the models to identify all tissues rather than considering tissues from similar anatomical areas as the same. Ultimately, the FRF similarity metrics are a novel approach for extracting features from BIS measurements that improved identification performance when considering both accuracy and capability of differentiating all tissues in the dataset.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559621","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":"3D-Printed Scaffold Mimicking IBD Gut Microenvironments: An In Vitro Model for Bacterial Bioink Growth.","authors":"Alejandro Garcia-Gonzalez, Mariana Jaquez-Sanchez, Axel Maya-Morales, Mariana S Flores-Jimenez, Yocanxochitl Perfecto-Avalos, Isaac Chairez-Oria, Abel Gutierrez-Vilchis, Ricardo Garcia-Gamboa","doi":"10.1109/EMBC53108.2024.10782731","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782731","url":null,"abstract":"<p><p>Inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract, affects millions worldwide and is linked to altered gut microbiota. This study explored the feasibility of a 3D-bioprinting scaffold containing Lactococcus lactis using an alginate-agar-soy trypticase bioink. The bioink exhibited high water absorption and adequate rheology, enabling successful bioprinting of scaffolds with robust structures. The scaffolds remained stable for 24 hours, allowing prolonged bacterial growth. L. lactis viability was confirmed by confocal microscopy, which revealed green fluorescence indicative of live bacteria even after 8 hours of culture within the scaffold. This suggests a supportive microenvironment for bacterial survival and potential proliferation. Compared to a 2D model, the 3D scaffold increased the number of colony-forming units (CFUs), indicating a more supportive environment for L. lactis growth. Overall, this study emphasizes the potential of 3D-printed bacterial scaffolds as a platform culture to assess the factors influencing the microbiota in various diseases.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559003","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 Wearable System for Monitoring Neurological Disorder Events with Multi-Class Classification Model in Daily Life.","authors":"Yonghun Song, Inyeol Yun, Sandra Giovanoli, Chris Awai Easthope, Yoonyoung Chung","doi":"10.1109/EMBC53108.2024.10782047","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782047","url":null,"abstract":"<p><p>Dysphagia and dysarthria are the prominent sequelae of neurological disorders. Treatment and rehabilitation of these impairments necessitate continuously monitoring symptoms related to swallowing and speaking. However, current medical technologies require large and diverse equipment to record these symptoms, which are predominantly limited to clinical environments. In this study, we propose an innovative wearable system for distinguishing neurological disorder events using a mechano-acoustic (MA) sensor and multi-class ensemble classification model. The MA sensor exhibits a high sensitivity to neck vibration without any interference from ambient sounds. A multi-class classification model was also developed to discern the symptoms from the recorded signals accurately. The proposed classification model is an ensemble neural network trained on waveforms and mel spectrograms. As a result, we achieve a high classification accuracy of 91.94%, surpassing the performance of previous single neural networks.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559011","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":"Acute Pain Recognition from Facial Expression Videos using Vision Transformers.","authors":"Ghazal Bargshady, Calvin Joseph, Niraj Hirachan, Roland Goecke, Raul Fernandez Rojas","doi":"10.1109/EMBC53108.2024.10781616","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781616","url":null,"abstract":"<p><p>Pain assessment is significant for patients and clinicians in diagnosis and treatment injuries and disease. It could facilitate a patient's treatment process by monitoring patients' pain levels in an accurate and regular manner. Automated detection of pain from facial expressions is a useful technique to assess pain of patients with communication disabilities. In this study, video vision transformers (ViViT) enhanced for pain recognition tasks are presented to capture spatio-temporal, facial information relevant to estimating the binary classification of pain and, thus, to provide valuable insights for automated estimation. The developed model has been trained and evaluated on two acute pain datasets, including 51 subjects using a newly collected pain intensity dataset designated as the AI4PAIN Challenge dataset, and 87 subjects from the BioVid Pain dataset. As an ablation study we used two baseline models, ResNet50 and a hybrid deep learning model based on the pretrained ResNet50+3DCNN. The results demonstrated that the proposed ViViT outperform the other models in pain detection by achieving accuracy = 66.96% for AI4PAIN dataset and accuracy = 79.95% for BioVid dataset.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559012","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":"Adaptive Impedance Matching with Fault Ride Through in Wireless Power Transfer for Implanted Medical Devices.","authors":"Han Wu, Yufei Cai, Haolun Wu, Sultan Mahmud, Ali Nezaratizadeh, Adam Khalifa","doi":"10.1109/EMBC53108.2024.10782376","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782376","url":null,"abstract":"<p><p>IMDs has found widespread application across various medical fields. Wirelessly powered implants are increasingly being developed to interface with neurons due to its small size. The matching network (MN) within the wireless IMD is a crucial component influencing system efficiency. Conventional approaches using fixed-value MNs struggle to adapt to changes in parameters and environment. This research proposes an adaptive algorithm-based MN that enabels the system to automatically track the maximum rectified voltage despite variations in frequency and inductor, as well as sampling errors due to random external interference. For the first time, an active voltage limiter has been integrated into the MN to reject excess power in order to safeguard the chip, rather than dissipating it as heat. Implemented in TSMC 65nm technology, this system can operate under ±15% inductance fluctuation and ±10% frequency fluctuation at 500 MHz, enabling unusable systems to obtain sufficient power. The chosen proof-of-concept for this work is a neural stimulating IMD but this approach can extend beyond this setup.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559016","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":"Improving Early Prediction of Abnormal Recovery after Appendectomy in Children using Real-world Data from Wearables.","authors":"Rui Hua, Megan K O'Brien, Michela Carter, J Benjamin Pitt, Soyang Kwon, Hassan M K Ghomrawi, Arun Jayaraman, Fizan Abdullah","doi":"10.1109/EMBC53108.2024.10782031","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782031","url":null,"abstract":"<p><p>Postoperative complications are primary concerns during early recovery from pediatric appendectomy. Identifying complications or symptoms of abnormal recovery typically relies on intermittent and subjective assessments from children and their caregivers, which may result in delayed diagnosis. Wearable devices can capture continuous and objective health measurements, which can be mined for early biomarkers of complications or symptoms using machine learning models. However, real-world datasets from wearables often have missing and imbalanced data, which can affect model performance and utility. We have recorded real-world Fitbit data from 93 children during the first 21 days following appendectomy for complicated appendicitis. This dataset included missing data (37.0% across all participants) and imbalanced data (2.7% of total days recorded from children exhibiting abnormal recovery). Aiming to improve early prediction of abnormal recovery, we extracted 143 daily features from the data, including Fitbit metrics of activity, heart rate, and sleep, as well as metrics derived from clinical knowledge. We trained a Balanced Random Forest classifier and tested different early prediction strategies for identifying abnormal recovery (complications or abnormal symptoms) 1-3 days before they were clinically diagnosed. The best-performing model predicted abnormal recovery three days before diagnosis at an accuracy of 87.5%, two days before at 76.4%, one day before at 85.7%, and on the day of diagnosis at 78.8%. The overall prediction accuracy was improved 10.1% compared to a previous study. With further development, this approach could be used to generate near real-time alerts of abnormal postoperative recovery to enhance pediatric care and clinical decision making.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559623","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":"Alternating Magnetic Field Generation and Interaction with Magnetic Polyelectrolyte Microcapsules.","authors":"Robert Powell, Neda Habibi","doi":"10.1109/EMBC53108.2024.10782949","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782949","url":null,"abstract":"<p><p>Layer-by-Layer (LbL) microcapsules with iron oxide nanoparticles facilitate smart drug delivery in the presence of an alternating magnetic field. A high-frequency alternating magnetic field (AMF) has been commonly used to induce hyperthermia for killing tumor tissues. This technique, however, could harm normal tissues. To achieve a safe drug delivery approach, we fabricate and test a device producing a low-frequency alternating magnetic field to interact with magnetic LbL capsules and induce smart release. The nanoparticles act as a vehicle that can be used to guide the microcapsule to targeted areas and as a mechanism to release compounds in the area of interest. Using scanning electron microscopy and fluorescence microscopy we can visualize the interaction of the alternating magnetic field with polyelectrolyte microcapsules containing iron oxide nanoparticles. We have created a system that we consider to be a robust framework for exploring the role of Layer-by-Layer microcapsules in targeted drug therapy.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559037","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 Basic Emotion via Machine Learning: Comparative Analysis of Number of Basic Emotions and Algorithms.","authors":"Caryn Vowles, Mackenzie Collins, T Claire Davies","doi":"10.1109/EMBC53108.2024.10782053","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782053","url":null,"abstract":"<p><p>This paper explores the use of machine learning (ML) methods to identify \"clusters\" of basic emotions based on pleasure, arousal, and dominance (PAD). The data was obtained from the Dataset for Emotion Analysis using Physiological Signals (DEAP), data collected within the Building and Designing Assistive Technology (BDAT) Lab using the International Affective Picture System (IAPS), and the scores of PAD from the IAPS. The objective is to develop an algorithm that maps a PAD score to clusters that express emotions, e.g., sadness or happiness. The elbow method was used to determine the optimal number of clusters (4-8), and nine different ML algorithms were compared. Decision Trees, polynomial support vector machines (SVMs) and linear SVMs provided accurate results. The Decision Tree demonstrated efficiency, during both testing and validation, in identifying the same clusters when analyzing both the DEAP and IAPS datasets. The dataset included limited data for each emotion creating the possibility of overfitting. However, when evaluating the results relative to previous research, the results added to the understanding of the nuances of emotion self-reporting and modelling.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559073","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":"Assessment of Force Feedback Models in a Haptic Device Using Alignment Accuracy and Brain Activity.","authors":"Harutake Nagai, Satoshi Miura","doi":"10.1109/EMBC53108.2024.10781934","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781934","url":null,"abstract":"<p><p>When controlling a robot's velocity, it is necessary to provide force feedback to the user, which shows the amount of input from the neutral position. In this paper, we introduce three distinct force feedback models, each with one or two parameters, and investigate the influence on the operability of the robot and brain activity according to the changes of the force feedback model using a haptic device. Participants in the alignment task performed tasks in which they aligned an object with a target position in virtual space using our developed interface while we measured the participant's operational performance and brain activation using functional near-infrared spectroscopy. We performed fitting using quadratic functions with the parameters of each model as design variables. The results of the alignment task demonstrated that two models achieved higher alignment performance depending on the position of the target from the neutral position and, for one model, brain activation changed significantly as the parameters changed.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559080","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}