scholarly journals Control of Newly-Designed Wearable Robotic Hand Exoskeleton Based on Surface Electromyographic Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Ke Li ◽  
Zhengzhen Li ◽  
Haibin Zeng ◽  
Na Wei
Keyword(s):  
Neuromuscular Disorders ◽  
Functional Integration ◽  
Near Neighbor ◽  
The Other ◽  
Advanced Technology ◽  
Support Vector ◽  
Human Hand ◽  
Robotic Hand ◽  
Hand Rehabilitation ◽  
Hand Exoskeleton

The human hand plays a role in a variety of daily activities. This intricate instrument is vulnerable to trauma or neuromuscular disorders. Wearable robotic exoskeletons are an advanced technology with the potential to remarkably promote the recovery of hand function. However, the still face persistent challenges in mechanical and functional integration, with real-time control of the multiactuators in accordance with the motion intentions of the user being a particular sticking point. In this study, we demonstrated a newly-designed wearable robotic hand exoskeleton with multijoints, more degrees of freedom (DOFs), and a larger range of motion (ROM). The exoskeleton hand comprises six linear actuators (two for the thumb and the other four for the fingers) and can realize both independent movements of each digit and coordinative movement involving multiple fingers for grasp and pinch. The kinematic parameters of the hand exoskeleton were analyzed by a motion capture system. The exoskeleton showed higher ROM of the proximal interphalangeal and distal interphalangeal joints compared with the other exoskeletons. Five classifiers including support vector machine (SVM), K-near neighbor (KNN), decision tree (DT), multilayer perceptron (MLP), and multichannel convolutional neural networks (multichannel CNN) were compared for the offline classification. The SVM and KNN had a higher accuracy than the others, reaching up to 99%. For the online classification, three out of the five subjects showed an accuracy of about 80%, and one subject showed an accuracy over 90%. These results suggest that the new wearable exoskeleton could facilitate hand rehabilitation for a larger ROM and higher dexterity and could be controlled according to the motion intention of the subjects.

scholarly journals Preliminary Results in Testing of a Novel Asymmetric Underactuated Robotic Hand Exoskeleton for Motor Impairment Rehabilitation

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1470 ◽  
Author(s):  
Flaviu Ionuț Birouaș ◽  
Radu Cătălin Țarcă ◽  
Simona Dzitac ◽  
Ioan Dzitac
Keyword(s):  
Mechanical Design ◽  
Experimental Testing ◽  
Motor Impairment ◽  
Human Hand ◽  
Underactuated System ◽  
Robotic Hand ◽  
Advantages And Disadvantages ◽  
Hand Exoskeleton ◽  
Robotic Devices ◽  
And Behavior

Robotic exoskeletons are a trending topic in both robotics and rehabilitation therapy. The research presented in this paper is a summary of robotic exoskeleton development and testing for a human hand, having application in motor rehabilitation treatment. The mechanical design of the robotic hand exoskeleton implements a novel asymmetric underactuated system and takes into consideration a number of advantages and disadvantages that arose in the literature in previous mechanical design, regarding hand exoskeleton design and also aspects related to the symmetric and asymmetric geometry and behavior of the biological hand. The technology used for the manufacturing and prototyping of the mechanical design is 3D printing. A comprehensive study of the exoskeleton has been done with and without the wearer’s hand in the exoskeleton, where multiple feedback sources are used to determine symmetric and asymmetric behaviors related to torque, position, trajectory, and laws of motion. Observations collected during the experimental testing proved to be valuable information in the field of augmenting the human body with robotic devices.

scholarly journals A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand–Motor Function

2019 ◽  
Vol 9 (18) ◽  
pp. 3751 ◽  
Author(s):  
Grant Rudd ◽  
Liam Daly ◽  
Vukica Jovanovic ◽  
Filip Cuckov
Keyword(s):  
Cost Effectiveness ◽  
Physical Therapy ◽  
Low Cost ◽  
Human Hand ◽  
Robotic Hand ◽  
Healthy Human ◽  
3D Printed ◽  
Hand Exoskeleton ◽  
Arduino Microcontroller ◽  
Work Done

We present the design and validation of a low-cost, customizable and 3D-printed anthropomorphic soft robotic hand exoskeleton for rehabilitation of hand injuries using remotely administered physical therapy regimens. The design builds upon previous work done on cable actuated exoskeleton designs by implementing the same kinematic functionality, but with the focus shifted to ease of assembly and cost effectiveness as to allow patients and physicians to manufacture and assemble the hardware necessary to implement treatment. The exoskeleton was constructed solely from 3D-printed and widely available off-the-shelf components. Control of the actuators was realized using an Arduino microcontroller, with a custom-designed shield to facilitate ease of wiring. Tests were conducted to verify that the range of motion of the digits and the forces exerted at the fingertip coincided with those of a healthy human hand.

scholarly journals A REVIEW REGARDING HAND EXOSKELETON TECHNOLOGIES FOR REHABILITATION

1970 ◽  
Vol 5 (1.) ◽  
Author(s):  
Flaviu Birouaș ◽  
Florin Avram ◽  
Arnold Nilgesz ◽  
Vlad Ovidiu Mihalca
Keyword(s):  
Medical Robotics ◽  
Short Review ◽  
Human Hand ◽  
Hand Rehabilitation ◽  
Robotic Rehabilitation ◽  
Current State ◽  
Hand Exoskeleton ◽  
Rehabilitation Devices ◽  
Robotic Devices ◽  
Future Direction

This paper will be presenting a short review regarding robotic rehabilitation devices. The focus of rehabilitation are aimed for the human hand, mainly for regaining motor functions by the aid of robotics. A comprehensive statistical study will be presented regarding tendencies in the field of rehabilitation,  medical robotics and technologies used for robotic exoskeletons based on existing published papers. A short review on existing practical examples is also presented. In the final part of the papers a short comparison is debated between soft robotic devices and rigid robotic devices used in hand rehabilitation. After presenting the review of the current state of the art a conclusion regarding the future direction of rehabilitation devices is proposed.

scholarly journals Longitudinal Changes of Tongue Thickness and Tongue Pressure in Neuromuscular Disorders

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
George Umemoto ◽  
Shinsuke Fujioka ◽  
Hajime Arahata ◽  
Nobutaka Sakae ◽  
Naokazu Sasagasako ◽  
...  
Keyword(s):  
Neuromuscular Disorders ◽  
The Other ◽  
Tongue Pressure ◽  
Swallowing Function ◽  
Longitudinal Changes ◽  
Swallowing Dysfunction ◽  
The Relationship ◽  
Lateral Sclerosis

Abstract Background Swallowing dysfunction is related to major cause of adverse events and an indicator of shorter survival among patients with neuromuscular disorders (NMD). It is critical to assess the swallowing function during disease progression, however, there are limited tools that can easily evaluate swallowing function without using videofluoroscopic or videoendoscopic examination. Here, we evaluated the longitudinal changes in tongue thickness (TT) and maximum tongue pressure (MTP) among patients with amyotrophic lateral sclerosis (ALS), myotonic dystrophy type 1 (DM1), and Duchenne muscular dystrophy (DMD). Methods Between 2010 and 2020, TT and MTP were measured from 21 ALS, 30 DM1, and 14 DMD patients (mean ages of 66.9, 44.5, and 21.4 years, respectively) at intervals of more than half a year. TT was measured, by ultrasonography, as the distance from the mylohyoid muscle raphe to the tongue dorsum, and MTP was determined by measuring the maximum compression on a small balloon when pressing the tongue against the palate. Then we examined the relationship between these evaluations and patient background and swallowing function. Results Mean follow-up periods were 24.0 months in the ALS group, 47.2 months in the DM1group, and 61.1 months in the DMD group. The DMD group demonstrated larger first TT than the other groups, while the DM1 group had lower first MTP than the ALS group. The ALS group showed a greater average monthly reduction in mean TT than the DM1 group and greater monthly reductions in mean body weight (BW) and MTP than the other groups. Significant differences between the first and last BW, TT, and MTP measures were found only in the ALS group. Conclusions This study suggests that ALS is associated with more rapid degeneration of tongue function over several years compared to DMD and DM1.

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

Managing to Avoid Innovation

1999 ◽  
Vol 13 (1) ◽  
pp. 25-32 ◽  
Author(s):  
J. H. Vaux ◽  
M. P. S. F. Gomes ◽  
R. J. Grieve ◽  
S. W. Woolgar
Keyword(s):  
The Other ◽  
Advanced Technology ◽  
Manufacturing Companies ◽  
Small Companies ◽  
Small Company ◽  
The West ◽  
Policy Makers ◽  
Technology And Innovation ◽  
Company Culture ◽  
The One

This paper addresses differences in the way that the problems of small UK firms are construed by policy makers on the one hand, and by the executives of small companies on the other. The authors employ a discursively-based analysis of interviews carried out with managers of small manufacturing companies in the West London area. They suggest that SME executives construe their attitudes to advanced technology and innovation within the terms of some clear, but implicit management values which tend to lead to the perception of innovation as a risk to be managed, rather than an opportunity to be exploited. It is suggested this has significant implications for attempts to change small company culture.

Cerebral Pulse Controlled Robotic Hand for Effective Paralytic Movements

2021 ◽  
Vol 9 (8) ◽  
pp. 2201-2204
Author(s):  
Abhay Patil
Keyword(s):  
Basic Assumption ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Handicapped People ◽  
Alpha Beta

Abstract: There are roughly 21 million handicapped people in India, which is comparable to 2.2% of the complete populace. These people are affected by various neuromuscular problems. To empower them to articulate their thoughts, one can supply them with elective and augmentative correspondence. For this, a Brain-Computer Interface framework (BCI) has been assembled to manage this specific need. The basic assumption of the venture reports the plan, working just as a testing impersonation of a man's arm which is intended to be powerfully just as kinematically exact. The conveyed gadget attempts to take after the movement of the human hand by investigating the signs delivered by cerebrum waves. The cerebrum waves are really detected by sensors in the Neurosky headset and produce alpha, beta, and gamma signals. Then, at that point, this sign is examined by the microcontroller and is then acquired onto the engineered hand by means of servo engines. A patient that experiences an amputee underneath the elbow can acquire from this specific biomechanical arm. Keywords: Brainwaves, Brain Computer Interface, Arduino, EEG sensor, Neurosky Mindwave Headset, Robotic arm

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