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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

Sensors, Vol. 21, Pages 1537: Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation
ordan The traditional systems used in the physiotherapy rehabilitation process are evolving towards more advanced systems that use virtual reality (VR) environments so that the patient in the rehabilitation process can perform various exercises in an interactive way, thus improving the patient’s motivation and reducing the therapist’s work. The paper presents a VR simulator for an intelligent robotic system of physiotherapeutic rehabilitation of the ankle of a person who has had a stroke. This simulator can interact with a real human subject by attaching a sensor that contains a gyroscope and accelerometer to ident...
Source: Sensors - February 23, 2021 Category: Biotechnology Authors: Florin Covaciu Adrian Pisla Anca-Elena Iordan Tags: Article Source Type: research

Sensors, Vol. 21, Pages 1111: Head-Mounted Display-Based Therapies for Adults Post-Stroke: A Systematic Review and Meta-Analysis
gan Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on post-stroke adult subjects via a head-mounted display ...
Source: Sensors - February 5, 2021 Category: Biotechnology Authors: Guillermo Palacios-Navarro Neville Hogan Tags: Review Source Type: research

Sensors, Vol. 21, Pages 914: Paddle Stroke Analysis for Kayakers Using Wearable Technologies
In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers’ motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers’ actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, includ...
Source: Sensors - January 29, 2021 Category: Biotechnology Authors: Long Liu Hui-Hui Wang Sen Qiu Yun-Cui Zhang Zheng-Dong Hao Tags: Article Source Type: research

Sensors, Vol. 21, Pages 460: Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction
wan We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (...
Source: Sensors - January 11, 2021 Category: Biotechnology Authors: Yun-Hsuan Chen Mohamad Sawan Tags: Review Source Type: research

Sensors, Vol. 20, Pages 2136: Detection of Atrial Fibrillation Using 1D Convolutional Neural Network
a Hsiao The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detectio...
Source: Sensors - April 9, 2020 Category: Biotechnology Authors: Chaur-Heh Hsieh Yan-Shuo Li Bor-Jiunn Hwang Ching-Hua Hsiao Tags: Article Source Type: research

Sensors, Vol. 20, Pages 2110: Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion
aXin Wang Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist’s motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist’s attitude information after sensor calibration, and then the motions of canoeist’s actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learn...
Source: Sensors - April 7, 2020 Category: Biotechnology Authors: Long Liu Sen Qiu ZheLong Wang Jie Li JiaXin Wang Tags: Article Source Type: research

Sensors, Vol. 19, Pages 3482: 3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology
Persson Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated t...
Source: Sensors - August 8, 2019 Category: Biotechnology Authors: Andreas Fhager Stefan Candefjord Mikael Elam Mikael Persson Tags: Article Source Type: research

Sensors, Vol. 19, Pages 3172: Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning
Kwon An engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, the parameter settings need to be updated for higher engine efficiency. Hence, to optimize parameter settings during operation, engine load information is necessary. In this paper, we propose a real-time engine load classification from sensed signals. For the classification, an artificial neural network is used and train...
Source: Sensors - July 17, 2019 Category: Biotechnology Authors: Syed Maaz Shahid Sunghoon Ko Sungoh Kwon Tags: Article Source Type: research

Sensors, Vol. 19, Pages 2573: Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning
-Chun Hsieh Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atheroscl...
Source: Sensors - June 5, 2019 Category: Biotechnology Authors: Yi-Zeng Hsieh Yu-Cin Luo Chen Pan Mu-Chun Su Chi-Jen Chen Kevin Li-Chun Hsieh Tags: Article Source Type: research

Sensors, Vol. 19, Pages 210: Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
Jörg Conradt Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely ...
Source: Sensors - January 8, 2019 Category: Biotechnology Authors: Zied Tayeb Juri Fedjaev Nejla Ghaboosi Christoph Richter Lukas Everding Xingwei Qu Yingyu Wu Gordon Cheng J örg Conradt Tags: Article Source Type: research

Sensors, Vol. 18, Pages 4107: Compressibility of High-Density EEG Signals in Stroke Patients
sco C. Morabito Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity. The primary goal of the present work was to investigate po...
Source: Sensors - November 23, 2018 Category: Biotechnology Authors: Nadia Mammone Simona De Salvo Cosimo Ieracitano Silvia Marino Emanuele Cartella Alessia Bramanti Roberto Giorgianni Francesco C. Morabito Tags: Article Source Type: research

Sensors, Vol. 17, Pages 1138: Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning
An important aspect in Human –Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot’s shell. In this work, we introduce a novel approach, whe re the audio acquired from contact microphones located in the robot’s shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed am...
Source: Sensors - May 16, 2017 Category: Biotechnology Authors: Fernando Alonso-Mart ín Juan Gamboa-Montero Jos é Castillo Álvaro Castro-González Miguel Salichs Tags: Article Source Type: research

Sensors, Vol. 17, Pages 582: Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis we...
Source: Sensors - March 12, 2017 Category: Biotechnology Authors: Yanran Li Xu Zhang Yanan Gong Ying Cheng Xiaoping Gao Xiang Chen Tags: Article Source Type: research

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