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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. 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. 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. 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. 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

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. 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. 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 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. 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. 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 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 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 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 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

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