scholarly journals A Knitted Sensing Glove for Human Hand Postures Pattern Recognition

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1364
Author(s):  
Seulah Lee ◽  
Yuna Choi ◽  
Minchang Sung ◽  
Jihyun Bae ◽  
Youngjin Choi
Keyword(s):  
Pattern Recognition ◽  
Confusion Matrix ◽  
Strain Sensors ◽  
Mixed Data ◽  
Human Hand ◽  
Flexible Sensors ◽  
Target Hand ◽  
The Cost ◽  
Grasp Posture ◽  
Conductive Yarn

In recent years, flexible sensors for data gloves have been developed that aim to achieve excellent wearability, but they are associated with difficulties due to the complicated manufacturing and embedding into the glove. This study proposes a knitted glove integrated with strain sensors for pattern recognition of hand postures. The proposed sensing glove is fabricated at all once by a knitting technique without sewing and bonding, which is composed of strain sensors knitted with conductive yarn and a glove body with non-conductive yarn. To verify the performance of the developed glove, electrical resistance variations were measured according to the flexed angle and speed. These data showed different values depending on the speed or angle of movements. We carried out experiments on hand postures pattern recognition for the practicability verification of the knitted sensing glove. For this purpose, 10 able-bodied subjects participated in the recognition experiments on 10 target hand postures. The average classification accuracy of 10 subjects reached 94.17% when their own data were used. The accuracy of up to 97.1% was achieved in the case of grasp posture among 10 target postures. When all mixed data from 10 subjects were utilized for pattern recognition, the average classification expressed by the confusion matrix arrived at 89.5%. Therefore, the comprehensive experimental results demonstrated the effectiveness of the knitted sensing gloves. In addition, it is expected to reduce the cost through a simple manufacturing process of the knitted sensing glove.

scholarly journals Designand implementation of wearable single chip system for sensing, processing and transmitting human gesture and motion data

2018 ◽  
Vol 7 (2.8) ◽  
pp. 335
Author(s):  
Venkata Snehith.H ◽  
Likita Ratna D ◽  
Syed Shameem
Keyword(s):  
Inertial Measurement Unit ◽  
Primary Objective ◽  
Measurement Unit ◽  
Human Hand ◽  
Single Chip ◽  
Motion Data ◽  
Wireless Transceiver ◽  
Wearable Electronic ◽  
Pressure Stress ◽  
The Cost

Our work aims at the design of wearable electronic modules that can be assembled as systems for sensing, processing, transmitting, actuating and mimicking human hand gestures and movements, to be used in various robotic, educational, military, medical, industrial, general and hobby applications, the secondary focus of our work is reducing the cost, complexity and assembly of such systems, which are already being used by research centers, laboratories and high-class industries all over the world while the primary objective is to bring these systems down to customizable modular components which could be assembled and combined the way the user wishes to and needs them to be, thereby bringing these concepts closer to a wider range of students, enthusiasts and hobbyists making it easy for them to understand and comprehend these concepts even at the beginner level. In our project, we used commonly available piezo-resistive materials and other household items to make force, pressure, stress, strain and bend sensors and appended them to an Inertial measurement unit, a microcontroller and a wireless transceiver all embedded onto a single chip, to create a simple sensing mechanism that could be worn on a human hand to sense, process and transmit the gestures for actuating and mimicking applications.

scholarly journals One-Step Laser Encapsulation of Nano-Cracking Strain Sensors

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2673 ◽  
Author(s):  
Chan Park ◽  
Hyunsuk Jung ◽  
Hyunwoo Lee ◽  
Sunguk Hong ◽  
Hyonguk Kim ◽  
...  
Keyword(s):  
Electronic Devices ◽  
Strain Sensors ◽  
Harsh Environments ◽  
Electrical Characteristics ◽  
Flexible Sensors ◽  
One Step ◽  
Wearable Electronic ◽  
Flexible Strain Sensor ◽  
Wearable Electronic Devices ◽  
Encapsulation Methods

Development of flexible strain sensors that can be attached directly onto the skin, such as skin-mountable or wearable electronic devices, has recently attracted attention. However, such flexible sensors are generally exposed to various harsh environments, such as sweat, humidity, or dust, which cause noise and shorten the sensor lifetimes. This study reports the development of a nano-crack-based flexible sensor with mechanically, thermally, and chemically stable electrical characteristics in external environments using a novel one-step laser encapsulation (OLE) method optimized for thin films. The OLE process allows simultaneous patterning, cutting, and encapsulating of a device using laser cutting and thermoplastic polymers. The processes are simplified for economical and rapid production (one sensor in 8 s). Unlike other encapsulation methods, OLE does not degrade the performance of the sensor because the sensing layers remain unaffected. Sensors protected with OLE exhibit mechanical, thermal, and chemical stability under water-, heat-, dust-, and detergent-exposed conditions. Finally, a waterproof, flexible strain sensor is developed to detect motions around the eye, where oil and sweat are generated. OLE-based sensors can be used in several applications that are exposed to a large amount of foreign matter, such as humid or sweaty environments.

scholarly journals TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis

2019 ◽  
Vol 15 (07) ◽  
pp. 124
Author(s):  
Deris Stiawan ◽  
Dimas Wahyudi ◽  
Ahmad Heryanto ◽  
Samsuryadi Samsuryadi ◽  
Mohd. Yazid Idris ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Internet Of Things ◽  
Confusion Matrix ◽  
Feature Selection Method ◽  
Signature Analysis ◽  
Rule Based ◽  
Average Percentage ◽  
Attack Pattern ◽  
Precision Level ◽  
Rate Evaluation

<p class="0abstract">Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenario normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.</p>

From Data Distributions to Regularization in Invariant Learning

1995 ◽  
Vol 7 (5) ◽  
pp. 974-981 ◽  
Author(s):  
Todd K. Leen
Keyword(s):  
Pattern Recognition ◽  
Probability Distribution ◽  
Cost Function ◽  
Training Data ◽  
Regularization Term ◽  
Constant Output ◽  
The Cost ◽  
Infinitesimal Transformations

Ideally pattern recognition machines provide constant output when the inputs are transformed under a group G of desired invariances. These invariances can be achieved by enhancing the training data to include examples of inputs transformed by elements of G, while leaving the corresponding targets unchanged. Alternatively the cost function for training can include a regularization term that penalizes changes in the output when the input is transformed under the group. This paper relates the two approaches, showing precisely the sense in which the regularized cost function approximates the result of adding transformed examples to the training data. We introduce the notion of a probability distribution over the group transformations, and use this to rewrite the cost function for the enhanced training data. Under certain conditions, the new cost function is equivalent to the sum of the original cost function plus a regularizer. For unbiased models, the regularizer reduces to the intuitively obvious choice—a term that penalizes changes in the output when the inputs are transformed under the group. For infinitesimal transformations, the coefficient of the regularization term reduces to the variance of the distortions introduced into the training data. This correspondence provides a simple bridge between the two approaches.

Conductive hydrogel-based flexible strain sensors with superior chemical stability and stretchability for mechanical sensing in corrosive solvents

2021 ◽  
Author(s):  
Hong Zhang ◽  
Mingqiang Yue ◽  
Tingting Wang ◽  
Jinqing Wang ◽  
Xianzhang Wu ◽  
...  
Keyword(s):  
Chemical Stability ◽  
Strain Sensors ◽  
Harsh Environments ◽  
Flexible Sensors ◽  
Practical Applications ◽  
Conductive Hydrogel ◽  
Mechanical Sensing

Wearable flexible sensors face many harsh environments in practical applications.

scholarly journals A Soft Robot Arm with Flexible Sensors for Master–Slave Operation

2021 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Yoshie Yamamoto ◽  
Shuichi Wakimoto ◽  
Takefumi Kanda ◽  
Daisuke Yamaguchi
Keyword(s):  
Feedback System ◽  
Strain Sensors ◽  
Artificial Muscles ◽  
Soft Robot ◽  
Robot Arm ◽  
Flexible Sensors ◽  
Flexible Materials ◽  
Fundamental Investigation ◽  
Wearable Interface ◽  
High Flexibility

In our study, a soft robot arm consisting of McKibben artificial muscles and a silicone rubber structure was developed. This robot arm can perform bending and twisting motions by ap-plying pneumatic pressure to the artificial muscles. The robot arm is made of flexible materials only, and therefore it has high flexibility and shape adaptability. In this report on the fundamental investigation of the master–slave feedback control of the soft robot arm for intentional operation, we focus on the bending motion of the soft robot arm. Three flexible strain sensors were placed on the soft robot arm for measuring the bending motion. By establishing a master–slave feedback system using the sensors, the bending motion of the soft robot arm followed the operator’s wrist motion detected via the wearable interface device.

Design of Hand Exoskeleton for Paralysis with Voice Pattern Recognition Control

2021 ◽  
Vol 50 ◽  
pp. 51-58
Author(s):  
T. Triwiyanto ◽  
Moch Prastawa Assalim Tetra Putra ◽  
Priyambada Cahya Nugraha ◽  
Bambang Guruh Irianto ◽  
Syaifudin ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Control System ◽  
Human Hand ◽  
Accuracy Rate ◽  
Push Button ◽  
Voice Command ◽  
Average Accuracy ◽  
Hand Exoskeleton ◽  
And Control ◽  
The Voice

The paralysis that occurs in the human limbs can be caused by strokes, injuries, age problems, and ligament damage. The purpose of this study is to design a hand exoskeleton as rehabilitation in patients who have had a stroke in hand. The contribution of this research is to design a hand exoskeleton with a control system to control mechanical movements using voice command so that it can be used by patients who have a stroke. To be used by patients who have a stroke, the researcher designed a control system using voice pattern recognition so that patients who have weak myoelectric signals can control the mechanics easily. This device uses the voice recognition module V3 as a voice command to control open and control close mechanical movements. This device is capable of recording and running commands directly by using the push button, which consists of a start, save, reset, open command record, and close command record. In the open command obtained an accuracy value of 97%, the close command obtained an accuracy value of 93%. The results showed that the voice commands given had an average accuracy rate of 95%. The results of this study can be implemented as a rehabilitation device for people who have had a stroke to try to mimic human hand movements.

Statistical Analysis of Micro-Crack Parameters for PPy-Coated Fiber Strain Sensors Applicable for Large Deformation

2011 ◽  
Vol 197-198 ◽  
pp. 1350-1353
Author(s):  
Jun Pu Wang ◽  
Pu Xue ◽  
Xiao Ming Tao
Keyword(s):  
Statistical Analysis ◽  
Large Deformation ◽  
Experimental Studies ◽  
Processing Technique ◽  
Strain Sensors ◽  
Image Processing Technique ◽  
Strain Sensing ◽  
Flexible Sensors ◽  
Micro Cracks ◽  
Conductive Fibers

The conductive fibers have great potential as flexible sensors. In our previous study, based on the experimental studies on sensing behavior of PPy (polypyrrole)-coated Lycra fibers, the mechanism of the strain sensing behavior was identified as open-close mechanism of the cracks on the coating layer when experiencing large deformation. In order to modeling its strain sensing behavior and describe quantitively the development of the micro-cracks, statistical analysis is conducted by image processing technique in MATLAB software. It was concluded that the variation of resistance is related to the micro parameters of the surface cracks, suck as the number, width and length of micro-cracks. These will be used in our further modeling work.

RECOGNITION OF SEMG HAND ACTIONS BASED ON CLOUD ADAPTIVE QUANTUM CHAOS IONS MOTION ALGORITHM OPTIMIZED SVM

2019 ◽  
Vol 19 (06) ◽  
pp. 1950047
Author(s):  
BINGZHU WANG ◽  
CHAO WANG ◽  
LU WANG ◽  
NENGGANG XIE ◽  
WEI WEI
Keyword(s):  
Pattern Recognition ◽  
Quantum Chaos ◽  
Original Position ◽  
Support Vector ◽  
Human Hand ◽  
Hand Motion ◽  
Svm Algorithm ◽  
Hand Grasp ◽  
Model Recognition ◽  
Semg Signals

In this study, in order to improve the accuracy of human hand motion pattern recognition, a novel pattern recognition method for optimizing the support vector machine (SVM) by using a cloud adaptive quantum chaos ions motion optimization (AQCIMO-SVM) algorithm is proposed. The maximum values of wavelet coefficients were extracted as feature samples from the de-noised surface electromyography (sEMG) signals, which were collected from the forearm muscles of several subjects, and then the extracted feature was inputted into an SVM to classify action recognition. In addition, the AQCIMO algorithm was applied to optimize the penalty parameters and the kernel parameters of the SVM, which are used to avoid the uncertainty and complexity of parameter selection and improve the recognition precision of the model, thus improving the model recognition accuracy. The simulation results demonstrated that the two types of movement, which included basic gestures (rest, hand grasp, hand extension, wrist down, and wrist up) and object grabbing gestures (pre-grab, grab, transport and place, release hand, and return to the original position) were successfully identified by the SVM method combined with the AQCIMO algorithm. Compared to mainstream and classic classifiers, namely, GA-SVM, PSO-SVM, and AFSA-SVM, the accuracy of the proposed method was higher by 4.2% to 8.2% than that of the aforementioned classifiers. Therefore, the AQCIMO-SVM algorithm can efficiently solve the problem of the classification of the action pattern of the sEMG signals, which has a very important practical value.

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