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Sensors, Vol. 22, Pages 9480: An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
ammad Usman Akram Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated auton...
Source: Sensors - December 4, 2022 Category: Biotechnology Authors: Amna Waheed Awan Syed Muhammad Usman Shehzad Khalid Aamir Anwar Roobaea Alroobaea Saddam Hussain Jasem Almotiri Syed Sajid Ullah Muhammad Usman Akram Tags: Article Source Type: research

Sensors, Vol. 22, Pages 9347: Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification
Elgendy An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system&a...
Source: Sensors - December 1, 2022 Category: Biotechnology Authors: Mohamed Hammad Souham Meshoul Piotr Dziwi ński Pawe ł Pławiak Ibrahim A. Elgendy Tags: Article Source Type: research

Sensors, Vol. 22, Pages 8868: Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions
This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., “Alert”, “Dynamic”, “Running”, “Low Intensity”) events. Manual coding evaluated stroke actions ...
Source: Sensors - November 16, 2022 Category: Biotechnology Authors: Thomas Perri Machar Reid Alistair Murphy Kieran Howle Rob Duffield Tags: Article Source Type: research

Sensors, Vol. 22, Pages 8733: Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides
u Eiichi Ishikawa In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface elect...
Source: Sensors - November 11, 2022 Category: Biotechnology Authors: Alexey Anastasiev Hideki Kadone Aiki Marushima Hiroki Watanabe Alexander Zaboronok Shinya Watanabe Akira Matsumura Kenji Suzuki Yuji Matsumaru Eiichi Ishikawa Tags: Article Source Type: research

Sensors, Vol. 22, Pages 8615: A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions
is Kyriazis Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario’s requirements and datasets, for efficient...
Source: Sensors - November 8, 2022 Category: Biotechnology Authors: Argyro Mavrogiorgou Athanasios Kiourtis Spyridon Kleftakis Konstantinos Mavrogiorgos Nikolaos Zafeiropoulos Dimosthenis Kyriazis Tags: Article Source Type: research

Sensors, Vol. 22, Pages 6960: Using Deep Learning to Predict Minimum Foot & ndash;Ground Clearance Event from Toe-Off Kinematics
Sensors, Vol. 22, Pages 6960: Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics Sensors doi: 10.3390/s22186960 Authors: Clement Ogugua Asogwa Hanatsu Nagano Kai Wang Rezaul Begg Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as ...
Source: Sensors - September 14, 2022 Category: Biotechnology Authors: Clement Ogugua Asogwa Hanatsu Nagano Kai Wang Rezaul Begg Tags: Article Source Type: research

Sensors, Vol. 22, Pages 6323: Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras
Ha Ryu The timed up-and-go (TUG) test is an efficient way to evaluate an individual’s basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual’s overall mobility. Moreover, the fine-grained consumption time of the individual subtasks in the TUG test may provide important clinical information, such as elapsed time and speed of each TUG subtask, which may not only assist professionals in clinical interventions but also distinguish the functional recovery of patients. To perform mor...
Source: Sensors - August 23, 2022 Category: Biotechnology Authors: Yoon Jeong Choi Yoo Sung Bae Baek Dong Cha Je Ha Ryu Tags: Article Source Type: research

Sensors, Vol. 22, Pages 5347: Surface-Free Multi-Stroke Trajectory Reconstruction and Word Recognition Using an IMU-Enhanced Digital Pen
n M. Eskofier Efficient handwriting trajectory reconstruction (TR) requires specific writing surfaces for detecting movements of digital pens. Although several motion-based solutions have been developed to remove the necessity of writing surfaces, most of them are based on classical sensor fusion methods limited, by sensor error accumulation over time, to tracing only single strokes. In this work, we present an approach to map the movements of an IMU-enhanced digital pen to relative displacement data. Training data is collected by means of a tablet. We propose several pre-processing and data-preparation methods to sync...
Source: Sensors - July 18, 2022 Category: Biotechnology Authors: Mohamad Wehbi Daniel Luge Tim Hamann Jens Barth Peter Kaempf Dario Zanca Bjoern M. Eskofier Tags: Article Source Type: research

Sensors, Vol. 22, Pages 5066: Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia
Aarne Feldheiser Compensated shock and hypovolaemia are frequent conditions that remain clinically undetected and can quickly cause deterioration of perioperative and critically ill patients. Automated, accurate and non-invasive detection methods are needed to avoid such critical situations. In this experimental study, we aimed to create a prediction model for stroke volume index (SVI) decrease based on electrical cardiometry (EC) measurements. Transthoracic echo served as reference for SVI assessment (SVI-TTE). In 30 healthy male volunteers, central hypovolaemia was simulated using a lower body negative pressure (LBN...
Source: Sensors - July 6, 2022 Category: Biotechnology Authors: Matthias Stetzuhn Timo Tigges Alexandru Gabriel Pielmus Claudia Spies Charlotte Middel Michael Klum Sebastian Zaunseder Reinhold Orglmeister Aarne Feldheiser Tags: Article Source Type: research

Sensors, Vol. 22, Pages 4789: A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance
This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression m...
Source: Sensors - June 24, 2022 Category: Biotechnology Authors: Thomas Bennett Praveen Kumar Virginia Ruiz Garate Tags: Article Source Type: research

Sensors, Vol. 22, Pages 4670: Stroke Risk Prediction with Machine Learning Techniques
gka A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that ...
Source: Sensors - June 21, 2022 Category: Biotechnology Authors: Elias Dritsas Maria Trigka Tags: Article Source Type: research

Sensors, Vol. 22, Pages 4310: Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning
In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respective...
Source: Sensors - June 7, 2022 Category: Biotechnology Authors: Hamada R. H. Al-Absi Mohammad Tariqul Islam Mahmoud Ahmed Refaee Muhammad E. H. Chowdhury Tanvir Alam Tags: Article Source Type: research

Sensors, Vol. 22, Pages 3368: Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback
Jiping Wang Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the sub...
Source: Sensors - April 28, 2022 Category: Biotechnology Authors: Kangjia Ding Bochao Zhang Zongquan Ling Jing Chen Liquan Guo Daxi Xiong Jiping Wang Tags: Article Source Type: research

Sensors, Vol. 22, Pages 2414: Sensing System for Plegic or Paretic Hands Self-Training Motivation
inik Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2&md...
Source: Sensors - March 21, 2022 Category: Biotechnology Authors: Igor Zubrycki Ewa Pr ączko-Pawlak Ilona Dominik Tags: Article Source Type: research

Sensors, Vol. 22, Pages 1776: Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device
In conclusion, Mobilenet would be a more efficient model than Resnet to classify arrhythmia in an embedded wearable device.
Source: Sensors - February 24, 2022 Category: Biotechnology Authors: Kwang-Sig Lee Hyun-Joon Park Ji Eon Kim Hee Jung Kim Sangil Chon Sangkyu Kim Jaesung Jang Jin-Kook Kim Seongbin Jang Yeongjoon Gil Ho Sung Son Tags: Article Source Type: research

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