High Degree of Freedom Hand Pose Tracking Using Limited Strain Sensing and Optical Training

2019 ◽  
Vol 19 (3) ◽  
Author(s):  
Wentai Zhang ◽  
Jonelle Z. Yu ◽  
Fangcheng Zhu ◽  
Yifang Zhu ◽  
Zhangsihao Yang ◽  
...  
Keyword(s):  
American Sign Language ◽  
Strain Gage ◽  
Support Vector ◽  
Svm Classifier ◽  
Leap Motion ◽  
Strain Gages ◽  
Joint Angles ◽  
Linear Quadratic ◽  
Feed Forward Neural Network ◽  
Pose Tracking

The ability to track human operators' hand usage when working in production plants and factories is critically important for developing realistic digital factory simulators as well as manufacturing process control. We propose a proof-of-concept instrumented glove with only a few strain gage sensors and a microcontroller that continuously tracks and records the hand configuration during actual use. At the heart of our approach is a trainable system that can predict the fourteen joint angles in the hand using only a small set of strain sensors. First, ten strain gages are placed at various joints in the hand to optimize the sensor layout using the English letters in the American Sign Language (ASL) as a benchmark for assessment. Next, the best sensor configurations for three through ten strain gages are computed using a support vector machine (SVM) classifier. Following the layout optimization, our approach learns a mapping between the sensor readouts to the actual joint angles optically captured using a Leap Motion system. Five regression methods including linear, quadratic, and neural regression are then used to train the mapping between the strain gage data and the corresponding joint angles. The final proposed model involves four strain gages mapped to the fourteen joint angles using a two-layer feed-forward neural network (NN).

High Degree of Freedom Hand Pose Tracking Using Limited Strain Sensing and Optical Training

2018 ◽  
Author(s):  
Wentai Zhang ◽  
Jonelle Z. Yu ◽  
Fangcheng Zhu ◽  
Yifang Zhu ◽  
Nurcan Gecer Ulu ◽  
...  
Keyword(s):  
American Sign Language ◽  
Strain Gauge ◽  
Strain Gauges ◽  
Support Vector ◽  
Leap Motion ◽  
Strain Sensing ◽  
Joint Angles ◽  
Linear Quadratic ◽  
Feed Forward Neural Network ◽  
Pose Tracking

The ability to track human operators’ hand usage when working in production plants and factories is critically important for developing realistic digital factory simulators as well as manufacturing process control. We propose an instrumented glove with only a few strain gauge sensors and a micro-controller that continuously tracks and records the hand configuration during actual use. At the heart of our approach is a trainable system that can predict the fourteen joint angles in the hand using only a small set of strain sensors. First, ten strain gauges are placed at the various joints in the hand to optimize the sensor layout using the English letters in the American Sign Language as a benchmark for assessment. Next, the best sensor configurations for three through ten strain gauges are computed using a support vector machine classifier. Following the layout optimization, our approach learns a mapping between the sensor readouts to the actual joint angles optically captured using a Leap Motion system. Three regression methods including linear, quadratic and neural regression are then used to train the mapping between the strain gauge data and the corresponding joint angles. The final proposed model involves four strain gauges mapped to the fourteen joint angles using a two-layer feed-forward neural network.

scholarly journals American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3554 ◽  
Author(s):  
Teak-Wei Chong ◽  
Boon-Giin Lee
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Recognition System ◽  
Support Vector ◽  
Leap Motion ◽  
American Sign ◽  
Language Recognition ◽  
Motion Controller ◽  
Sign Language Recognition ◽  
Leap Motion Controller

Sign language is intentionally designed to allow deaf and dumb communities to convey messages and to connect with society. Unfortunately, learning and practicing sign language is not common among society; hence, this study developed a sign language recognition prototype using the Leap Motion Controller (LMC). Many existing studies have proposed methods for incomplete sign language recognition, whereas this study aimed for full American Sign Language (ASL) recognition, which consists of 26 letters and 10 digits. Most of the ASL letters are static (no movement), but certain ASL letters are dynamic (they require certain movements). Thus, this study also aimed to extract features from finger and hand motions to differentiate between the static and dynamic gestures. The experimental results revealed that the sign language recognition rates for the 26 letters using a support vector machine (SVM) and a deep neural network (DNN) are 80.30% and 93.81%, respectively. Meanwhile, the recognition rates for a combination of 26 letters and 10 digits are slightly lower, approximately 72.79% for the SVM and 88.79% for the DNN. As a result, the sign language recognition system has great potential for reducing the gap between deaf and dumb communities and others. The proposed prototype could also serve as an interpreter for the deaf and dumb in everyday life in service sectors, such as at the bank or post office.

Wayang Robot with Gamelan Music Pattern Recognition

2017 ◽  
Vol 29 (1) ◽  
pp. 137-145 ◽  
Author(s):  
Tito Pradhono Tomo ◽  
◽  
Alexander Schmitz ◽  
Guillermo Enriquez ◽  
Shuji Hashimoto ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Degrees Of Freedom ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Intangible Cultural Heritage ◽  
Feed Forward Neural Network ◽  
Mel Frequency Cepstral Coefficients ◽  
Puppet Theater ◽  
Music Patterns

[abstFig src='/00290001/13.jpg' width='245' text='Wayang robot' ] This paper proposes a way to protect endangered wayang puppet theater, an intangible cultural heritage from Indonesia, by turning a robot into a puppeteer successor. We developed a seven degrees-of-freedom (DOF) manipulator to actuate the sticks attached to the wayang puppet body and hands. The robot can imitate 8 distinct human puppeteer’s manipulations. Furthermore, we developed a gamelan music pattern recognition, towards a robot that can perform based on the gamelan music. In the offline experiment, we extracted energy (time domain), spectral rolloff, 13 Mel-frequency cepstral coefficients (MFCCs), and the harmonic ratio from 5 s long clips, every 0.025 s, with a window length of 1 s, for a total of 2576 features. Two classifiers (3 layers feed-forward neural network (FNN) and multi-class Support Vector Machine (SVM)) were compared. The SVM classifier outperformed the FNN classifier with a recognition rate of 96.4% for identifying the three different gamelan music patterns.

scholarly journals Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition

Computers ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Rahul Raj Devaraja ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius
Keyword(s):  
Shape Recognition ◽  
Angular Position ◽  
Support Vector ◽  
Svm Classifier ◽  
Robotic Hand ◽  
Leap Motion ◽  
Shape Identification ◽  
Object Shape ◽  
Everyday Objects ◽  
Artificial Hand

We developed an anthropomorphic multi-finger artificial hand for a fine-scale object grasping task, sensing the grasped object’s shape. The robotic hand was created using the 3D printer and has the servo bed for stand-alone finger movement. The data containing the robotic fingers’ angular position are acquired using the Leap Motion device, and a hybrid Support Vector Machine (SVM) classifier is used for object shape identification. We trained the designed robotic hand on a few monotonous convex-shaped items similar to everyday objects (ball, cylinder, and rectangular box) using supervised learning techniques. We achieve the mean accuracy of object shape recognition of 94.4%.

scholarly journals Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

2012 ◽  
Vol 17 (4) ◽  
pp. 9-16 ◽  
Author(s):  
Gabriel Deak ◽  
Kevin Curran ◽  
Joan Condell ◽  
Daniel Deak ◽  
Piotr Kiedrowski
Keyword(s):  
Support Vector Machine ◽  
Probabilistic Neural Network ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Quadratic ◽  
Wireless Signal ◽  
Device Free ◽  
Tracking Devices ◽  
Passive Localization

Abstract The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.

Classification of Sleep Apnea Types Using Clustering with SVM Classifier

2012 ◽  
Vol 1 (1) ◽  
pp. 39-48
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Sleep Apnea ◽  
High Reliability ◽  
Component Analysis ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Quadratic ◽  
Fast Independent Component Analysis ◽  
Obstructive Sleep

A new automated approach for the polysomnography (PSG) characterization and classification with the combination of FastICA, clustering and support vector machines (SVM) is presented in this paper. The method is based on two key steps. In the first step, the authors adopt the Principal Component Analysis (PCA) and Fast Independent Component Analysis (FastICA) approaches to separate and transform the original inputs into uncorrelated and mutually independent new features. In the second step, they utilize the K_Means clustering combined with Support Vector Machine (SVM) to build the proposed classifier. Multiple SVM kernels such as the linear, quadratic, polynomial, and radial basic functions are used for the classification of central and obstructive sleep apnea. Their results suggest the high reliability and high classification accuracy of polynomial kernel.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 739
Author(s):  
Alessandro Bevilacqua ◽  
Margherita Mottola ◽  
Fabio Ferroni ◽  
Alice Rossi ◽  
Giampaolo Gavelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
B Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Primary Role ◽  
High B Value ◽  
Wilcoxon Rank Sum Test ◽  
Apparent Diffusion ◽  
Clinically Significant

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

Sign in / Sign up

Export Citation Format

Share Document