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Total 60 results found since Jan 2013.

Sensors, Vol. 22, Pages 96: Artifacts in EEG-Based BCI Therapies: Friend or Foe?
In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While inform...
Source: Sensors - December 24, 2021 Category: Biotechnology Authors: Eric James McDermott Philipp Raggam Sven Kirsch Paolo Belardinelli Ulf Ziemann Christoph Zrenner Tags: Article Source Type: research

Sensors, Vol. 21, Pages 8507: A Review on Computer Aided Diagnosis of Acute Brain Stroke
o U. Rajendra Acharya Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in s...
Source: Sensors - December 20, 2021 Category: Biotechnology Authors: Mahesh Anil Inamdar Udupi Raghavendra Anjan Gudigar Yashas Chakole Ajay Hegde Girish R. Menon Prabal Barua Elizabeth Emma Palmer Kang Hao Cheong Wai Yee Chan Edward J. Ciaccio U. Rajendra Acharya Tags: Review Source Type: research

Sensors, Vol. 21, Pages 7784: Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning
alle Åström Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detectio...
Source: Sensors - November 23, 2021 Category: Biotechnology Authors: Johan Wasselius Eric Lyckeg ård Finn Emma Persson Petter Ericson Christina Brog årdh Arne G. Lindgren Teresa Ullberg Kalle Åström Tags: Article Source Type: research

Sensors, Vol. 21, Pages 6636: The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait
Smit Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used t...
Source: Sensors - October 6, 2021 Category: Biotechnology Authors: Hartog Harlaar Smit Tags: Article Source Type: research

Sensors, Vol. 21, Pages 5334: Prediction of Myoelectric Biomarkers in Post-Stroke Gait
This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable...
Source: Sensors - August 7, 2021 Category: Biotechnology Authors: Iqram Hussain Se-Jin Park Tags: Article Source Type: research

Sensors, Vol. 21, Pages 5302: Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques
uang Zhou Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection ...
Source: Sensors - August 5, 2021 Category: Biotechnology Authors: Yaru Yue Chengdong Chen Pengkun Liu Ying Xing Xiaoguang Zhou Tags: Article Source Type: research

Sensors, Vol. 21, Pages 5253: Intention Prediction and Human Health Condition Detection in Reaching Tasks with Machine Learning Techniques
t-Bauzel Detecting human motion and predicting human intentions by analyzing body signals are challenging but fundamental steps for the implementation of applications presenting human–robot interaction in different contexts, such as robotic rehabilitation in clinical environments, or collaborative robots in industrial fields. Machine learning techniques (MLT) can face the limit of small data amounts, typical of this kind of applications. This paper studies the illustrative case of the reaching movement in 10 healthy subjects and 21 post-stroke patients, comparing the performance of linear discriminant analysis (LDA) ...
Source: Sensors - August 4, 2021 Category: Biotechnology Authors: Federica Ragni Leonardo Archetti Agn ès Roby-Brami Cinzia Amici Ludovic Saint-Bauzel Tags: Article Source Type: research

Sensors, Vol. 21, Pages 5130: Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.
Source: Sensors - July 29, 2021 Category: Biotechnology Authors: Hye-Mee Kwon Woo-Young Seo Jae-Man Kim Woo-Hyun Shim Sung-Hoon Kim Gyu-Sam Hwang Tags: Article Source Type: research

Sensors, Vol. 21, Pages 4482: NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks
tavo Nonato A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In th...
Source: Sensors - June 30, 2021 Category: Biotechnology Authors: Rodrigo Colnago Contreras Avinash Parnandi Bruno Gomes Coelho Claudio Silva Heidi Schambra Luis Gustavo Nonato Tags: Article Source Type: research

Sensors, Vol. 21, Pages 4372: Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection
olorado Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine...
Source: Sensors - June 26, 2021 Category: Biotechnology Authors: Jenny Carolina Castiblanco Ivan Fernando Mondragon Catalina Alvarado-Rojas Julian D. Colorado Tags: Article Source Type: research

Sensors, Vol. 21, Pages 4269: Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals
Jae-Hak Yu The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and covid-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderl...
Source: Sensors - June 22, 2021 Category: Biotechnology Authors: Yoon-A Choi Se-Jin Park Jong-Arm Jun Cheol-Sig Pyo Kang-Hee Cho Han-Sung Lee Jae-Hak Yu Tags: Article Source Type: research

Sensors, Vol. 21, Pages 3444: Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases
Chiara Carrozza Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic s...
Source: Sensors - May 15, 2021 Category: Biotechnology Authors: Thomas Bowman Elisa Gervasoni Chiara Arienti Stefano Giuseppe Lazzerini Stefano Negrini Simona Crea Davide Cattaneo Maria Chiara Carrozza Tags: Systematic Review Source Type: research

Sensors, Vol. 21, Pages 3130: A System for Neuromotor Based Rehabilitation on a Passive Robotic Aid
Moroni In the aging world population, the occurrence of neuromotor deficits arising from stroke and other medical conditions is expected to grow, demanding the design of new and more effective approaches to rehabilitation. In this paper, we show how the combination of robotic technologies with progress in exergaming methodologies may lead to the creation of new rehabilitation protocols favoring motor re-learning. To this end, we introduce the Track-Hold system for neuromotor rehabilitation based on a passive robotic arm and integrated software. A special configuration of weights on the robotic arm fully balances the w...
Source: Sensors - April 30, 2021 Category: Biotechnology Authors: Marco Righi Massimo Magrini Cristina Dolciotti Davide Moroni Tags: Article Source Type: research

Sensors, Vol. 21, Pages 3121: Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks
Klemke Beginner table-tennis players require constant real-time feedback while learning the fundamental techniques. However, due to various constraints such as the mentor’s inability to be around all the time, expensive sensors and equipment for sports training, beginners are unable to get the immediate real-time feedback they need during training. Sensors have been widely used to train beginners and novices for various skills development, including psychomotor skills. Sensors enable the collection of multimodal data which can be utilised with machine learning to classify training mistakes, give feedback, and furthe...
Source: Sensors - April 30, 2021 Category: Biotechnology Authors: Khaleel Asyraaf Mat Sanusi Daniele Di Mitri Bibeg Limbu Roland Klemke Tags: Article Source Type: research

Sensors, Vol. 21, Pages 2084: Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges
Astaras Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on under...
Source: Sensors - March 16, 2021 Category: Biotechnology Authors: Nizamis Athanasiou Almpani Dimitrousis Astaras Tags: Review Source Type: research