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Source: Sensors
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Total 60 results found since Jan 2013.
Sensors, Vol. 23, Pages 7946: Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients
In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shaple...
Source: Sensors - September 17, 2023 Category: Biotechnology Authors: Chien Wei Oei Eddie Yin Kwee Ng Matthew Hok Shan Ng Ru-San Tan Yam Meng Chan Lai Gwen Chan Udyavara Rajendra Acharya Tags: Communication Source Type: research
Sensors, Vol. 23, Pages 7872: Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation
kubic
Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients’ health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily a...
Source: Sensors - September 14, 2023 Category: Biotechnology Authors: Rachel Proffitt Mengxuan Ma Marjorie Skubic Tags: Article Source Type: research
Sensors, Vol. 23, Pages 6793: Performance Evaluation for Clinical Stroke Rehabilitation via an Automatic Mobile Gait Trainer
Fu-Cheng Wang
This paper investigates the clinical efficacy of an automatic mobile trainer for gait training in stroke patients. Neuro-Developmental Treatment (NDT) is a rehabilitation method for stroke patients that enhances motor learning through repeated practice. Despite the proven effectiveness of therapist-assisted NDT, it is labor-intensive and demands health resources. Therefore, we developed automatic trainers based on NDT principles to perform gait training. This paper modifies the mobile trainer’s intervention patterns to improve the subject’s longitudinal gait symmetry, lateral p...
Source: Sensors - July 29, 2023 Category: Biotechnology Authors: Chih-Jen Shih You-Chi Li Wei Yuan Szu-Fu Chen Ang-Chieh Lin Tzu-Tung Lin Fu-Cheng Wang Tags: Article Source Type: research
Sensors, Vol. 23, Pages 6110: Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice
The objective of this study was to develop algorithms to enable the objective identification of task type and quality. Twenty neurotypical participants wore an IMU sensor on the wrist and performed four representative tasks in prescribed fashions that mimicked correct, compensatory, and incomplete movement qualities typically seen in stroke survivors. LSTM classifiers were trained to identify the task being performed and its movement quality. Our models achieved an accuracy of 90.8% for task identification and 84.9%, 81.1%, 58.4%, and 73.2% for movement quality classification for the four tasks for unseen participants. The...
Source: Sensors - July 3, 2023 Category: Biotechnology Authors: Mingqi Li Gabrielle Scronce Christian Finetto Kristen Coupland Matthew Zhong Melanie E. Lambert Adam Baker Feng Luo Na Jin Seo Tags: Article Source Type: research
Sensors, Vol. 23, Pages 5618: Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
s D. Zink
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insu...
Source: Sensors - June 15, 2023 Category: Biotechnology Authors: Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink Tags: Article Source Type: research
Sensors, Vol. 23, Pages 5513: Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets
mmadi
Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combin...
Source: Sensors - June 12, 2023 Category: Biotechnology Authors: Najmeh Razfar Rasha Kashef Farah Mohammadi Tags: Article Source Type: research
Sensors, Vol. 23, Pages 4042: Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
Changcheng Shi
Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome....
Source: Sensors - April 17, 2023 Category: Biotechnology Authors: Yang Hu Jingyan Meng Guoning Li Dazheng Zhao Guang Feng Guokun Zuo Yunfeng Liu Jiaji Zhang Changcheng Shi Tags: Article Source Type: research
Sensors, Vol. 23, Pages 3500: A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform
il Roushdy
Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementa...
Source: Sensors - March 27, 2023 Category: Biotechnology Authors: Bassant M. Elbagoury Luige Vladareanu Victor Vl ădăreanu Abdel Badeeh Salem Ana-Maria Travediu Mohamed Ismail Roushdy Tags: Article Source Type: research
Sensors, Vol. 23, Pages 1289: Time-Based and Path-Based Analysis of Upper-Limb Movements during Activities of Daily Living
Mihelj
Patients after stroke need to re-learn functional movements required for independent living throughout the rehabilitation process. In the study, we used a wearable sensory system for monitoring the movement of the upper limbs while performing activities of daily living. We implemented time-based and path-based segmentation of movement trajectories and muscle activity to quantify the activities of the unaffected and the affected upper limbs. While time-based segmentation splits the trajectory in quants of equal duration, path-based segmentation isolates completed movements. We analyzed the hand movement path and ...
Source: Sensors - January 23, 2023 Category: Biotechnology Authors: Sebastjan Šlajpah Eva Čebašek Marko Munih Matja ž Mihelj Tags: Article Source Type: research
Sensors, Vol. 23, Pages 1161: Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction
In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique’s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synt...
Source: Sensors - January 19, 2023 Category: Biotechnology Authors: Elias Dritsas Maria Trigka Tags: Article Source Type: research
Sensors, Vol. 23, Pages 857: A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries
Qiang Fang
The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). Th...
Source: Sensors - January 11, 2023 Category: Biotechnology Authors: Seedahmed S. Mahmoud Raphael F. Pallaud Akshay Kumar Serri Faisal Yin Wang Qiang Fang Tags: Article Source Type: research
Sensors, Vol. 23, Pages 643: An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications
rocco
Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, ...
Source: Sensors - January 6, 2023 Category: Biotechnology Authors: Álvaro Yago Ruiz Marta Cavagnaro Lorenzo Crocco Tags: Article Source Type: research
Sensors, Vol. 23, Pages 536: A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review
This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensi...
Source: Sensors - January 3, 2023 Category: Biotechnology Authors: Jia Min Yen Jeong Hoon Lim Tags: Review Source Type: research
Sensors, Vol. 22, Pages 9891: Improving Inertial Sensor-Based Activity Recognition in Neurological Populations
This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. A...
Source: Sensors - December 15, 2022 Category: Biotechnology Authors: Celik Aslan Sabanci Stuart Woo Godfrey Tags: Article Source Type: research
Sensors, Vol. 22, Pages 9859: Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal
This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cor...
Source: Sensors - December 15, 2022 Category: Biotechnology Authors: Mohammed Saidul Islam Iqram Hussain Md Mezbaur Rahman Se Jin Park Md Azam Hossain Tags: Article Source Type: research
