Classification of Grasp Patterns using sEMG

•Download as PPTX, PDF•

1 like•415 views

This document summarizes research on classifying grasp patterns using surface electromyography (sEMG) data. The goal was to build a classification model that identifies spherical and tip grasps. A male subject performed each grasp type 100 times daily for 3 days, providing 600 total instances. A hidden Markov model was used to classify the grasps, with 90% of data for training and 10% for testing. The model achieved 73.3% overall accuracy, with higher accuracy for spherical grasps. Suggestions for improving the model included adding more features and stratifying the training/test sets.

Engineering

Classification of Grasp Patterns
using sEMG
Priyanka R. Reddy

Motivation
• Build a classification model that identifies
spherical and tip grasps from surface
electromyography (sEMG) data

Types of Grasps
Spherical (Power grip) Tip (Precision grip)
Image: https://www.studyblue.com/notes/note/n/grasps-
updated-for-human-occ/deck/4455956
Image:
https://www.studyblue.com/notes/note/n/grasps-
updated-for-human-occ/deck/4455956

Dataset
• Healthy, male subject performs each grasp 100 times daily for three
days
• 600 instances (tip and spherical grasp data combined)
• Dataset was originally band-passed filtered to eliminate line
artifacts
Reference: C. Sapsanis. 'Recognition of basic hand movements using electromyography'. 2013.

Building a Classifier
1. Specify network model
2. Data pre-processing and Feature Extraction
3. Create Training and Test Sets
4. Parameterize the network
5. Validate and query model results

Hidden Markov
Model
• Simplest form of a
Dynamic Bayesian
Network
• Utilizes local conditional
probability densities to
compactly specify a
joint distribution
• Latent variables are
discrete random
variables
• Observed variables are
continuous random
variables
  

N
n
N
n
nnnnNN goPggPgPggooP
2 1
1111 )/()]/()[(),...,,,...,(

Model Variables and Feature
Extraction
Feature Extraction:
𝐼𝐸𝑀𝐺 =
𝑛=1
𝑁
𝑥 𝑛
used for observations denoted 𝑂 𝑛 in HMM
𝐺 𝑛 is a binary random variable taking values in the set {0,1}
where
0 – spherical grasp
1 – tip grasp
Reference: A. Phinyomark, C. Limsakul, and P. Phukpataranont. “A Novel Feature Extraction for Robust EMG Pattern
Recognition.” Journal of Computing. Volume 1. Issue 1, December 2009.

Training and Test sets
• Randomly sampled dataset
• 90 percent of data for training
• Remaining 10 percent used for testing

Parameterization
• Goal: fit the joint probability distribution to
the data, i.e. find the parameters
• Bayesian Logic (BLOG) computes posterior
distributions based on specified priors and
observation values using sampling algorithms
• Particle filter chosen to run the HMM

Computing Posterior Distributions
Specified priors in BLOG:
P(𝐺1) – probability mass function (pmf)
𝑃(𝐺 𝑛|𝐺 𝑛−1) – conditional probability table
𝑃(𝑂𝑛|𝐺 𝑛) – Gaussian distribution
=
1
𝜎 2𝜋
𝑒− (𝑥−𝜇 𝑖)2
2𝜎2 , i = 0, 1
𝑃(𝐺 𝑛|𝑂 𝑛) ∝ 𝑃(𝑂𝑛|𝐺 𝑛) 𝑃(𝐺 𝑛 )

Results
Overall Model Accuracy: 73.3%
Grasp Spherical Tip
Sensitivity 91% 58%
False Positive 42% 1%
False Negative 1% 42%
Specificity 58% 91%

Conclusion
• Current model predicts spherical grasps more
accurately than tip grasps
• Beneficial for picking up large objects as spherical
grasps are more effective for this task as
compared to tip grasps
• Improvement can be made to increase accuracy
– 1. Include additional features to feature vector and try
various combinations of vectors
– 2. Stratify the random sample when creating training
and test sets to ensure data from both groups is
represented

This study used artificial neural networks (ANNs) and instance-based learning (IBL) models to forecast significant wave heights 1, 3, and 6 hours ahead using wind and wave data from buoys located in the Caspian Sea. The ANNs performed slightly better than the IBL models. The models performed better at a deep water location compared to a shallow water location near the coast, where other environmental factors may influence wave patterns beyond wind conditions alone. While the data-driven methods showed potential, more comprehensive historical data could improve the accuracy of wind-wave forecasting in the Caspian Sea region.

Training algorithms for Neural Networks

Mrinmoy Majumder

ICML 2017 Meta network

Katy Lee

1) The Meta Network model proposes a two-level learning approach for few-shot learning. It includes a slow-learning meta-learner and a fast-learning base learner. 2) The meta-learner learns to generate fast weights for the base learner using gradient-based meta information from previous tasks. It stores these weights in a memory indexed by task embeddings. 3) Experiments on few-shot classification datasets like Omniglot and MiniImageNet demonstrate the model can learn new concepts from very few examples through fast adaptation of the base learner's weights.

Contrast enhancement and fpga implementation of mammogram images for early de...

Ranjitha Nag

This document describes a proposed method for contrast enhancement of mammogram images using FPGA implementation to aid in early detection of breast cancer. The method involves histogram equalization techniques at both the global and local level on mammogram images, with the goal of improving image quality and visual details while preserving local information. Experimental results on sample mammogram images demonstrate that the local histogram equalization method produces enhanced images with equal or higher PSNR compared to global histogram equalization alone. Tools like Matlab and Modelsim were used to develop and test the algorithm.

Learning to Learn by Gradient Descent by Gradient Descent

Katy Lee

This document discusses learning to learn by training a neural network (LSTM) to be an optimizer that learns update rules rather than using hand-designed update rules. The optimizer takes gradients as input and outputs updates to the parameters of the optimizee network. The optimizer is trained end-to-end using its trajectory optimization objective. Experiments show the learned optimizer can generalize to different network architectures but not different activation functions. The conclusion suggests emailing authors if confused by typos.

The Right Way

tim_morris

Matineh Shaker, Artificial Intelligence Scientist, Bonsai at MLconf SF 2017

MLconf

This document discusses deep reinforcement learning and concept network reinforcement learning. It begins with an introduction to reinforcement learning concepts like Markov decision processes and value-based methods. It then describes Concept-Network Reinforcement Learning which decomposes complex tasks into high-level concepts or actions. This allows composing existing solutions to sub-problems without retraining. The document provides examples of using concept networks for lunar lander and robot pick-and-place tasks. It concludes by discussing how concept networks can improve sample efficiency, especially for sparse reward problems.

Gaze-Net: Appearance-Based Gaze Estimation using CapsuleNetworks

Rajith Bhanuka Mahanama

This document describes a study that used capsule networks to estimate gaze from eye images. The researchers developed a two-step approach called Gaze-Net that first classifies gaze direction and then estimates gaze. Gaze-Net was trained on a large dataset and achieved good accuracy. It was also able to be personalized to new users through transfer learning, improving performance on a separate dataset. The study demonstrated that ocular images contain sufficient information for decoding head pose and eye orientation to estimate gaze direction.

Cross-validation aggregation combines the benefits of cross-validation and forecast aggregation. It saves the predictions from models estimated on different cross-validation folds and averages these predictions to obtain the final forecast. Empirical results on 111 time series show that cross-validation aggregation outperforms simple model averaging and bagging, with the lowest errors on validation sets. Different cross-validation aggregation methods perform best depending on data characteristics like time series length and forecast horizon.

KCC2017 28APR2017

JEE HYUN PARK

This document evaluates the performance of deep learning algorithms and data preprocessing for demand power prediction. Experiments were conducted using raw and preprocessed daily electricity load, temperature, and weather data from Australia. Recurrent neural networks and convolutional neural networks generally had better prediction accuracy when trained on preprocessed rather than raw data. Preprocessing scaled the temperature data and increased the data points, leading to more stable and accurate results across all tested algorithms. The best performance was achieved using a recurrent neural network on preprocessed data. Further analysis of extreme learning machine algorithms was recommended.

Review : Prototype Mixture Models for Few-shot Semantic Segmentation

Dongmin Choi

230727_HB_JointJournalClub.pptx

Network Science Lab, The Catholic University of Korea

1) The document proposes a graph-based method using graph convolutional networks to address challenges in sequential recommendation, such as extracting implicit preferences from long behavior sequences and adapting to changing user preferences over time. 2) It constructs an interest graph from user behaviors and designs an attentive graph convolutional network and dynamic pooling technique to aggregate implicit signals into explicit preferences. 3) Experimental results on two large-scale datasets show the proposed method significantly outperforms state-of-the-art sequential recommendation methods.

Grid search.pptx

AbithaSam

Grid search is a technique for finding optimal hyperparameters for a machine learning model. It involves specifying a range of values for each hyperparameter and evaluating the model's performance on all combinations of these values, using a metric like accuracy. The best-performing combination is selected. Grid search can be computationally expensive but produces reproducible results. Scikit-learn makes grid search easy to implement using GridSearchCV, and allows incorporating cross-validation to avoid overfitting and get a more accurate assessment of model performance.

Using HOG Descriptors on Superpixels for Human Detection of UAV Imagery

Wai Nwe Tun

This document presents a system for human detection in UAV imagery using HOG descriptors calculated for superpixels extracted via SLIC clustering. The system extracts superpixels, calculates HOG descriptors for each, and classifies the descriptors using an AdaBoost classifier. Evaluation on two datasets shows the superpixel-based approach achieves 3% higher accuracy than the original HOG method while requiring more computational time for training.

Mattar_PhD_Thesis

Marwan Mattar

Marwan Mattar presented his PhD thesis defense on unsupervised joint alignment, clustering, and feature learning. His research goal was to develop an unsupervised data set-agnostic processing module that includes alignment, clustering, and feature learning. He developed techniques for joint alignment of data using transformations, clustering data in an unsupervised manner, and learning features from the data. His techniques were shown to outperform other methods on tasks involving time series classification, face verification, and clustering of handwritten digits and ECG heart data.

PNN and inversion-B

Stig-Arne Kristoffersen

The document discusses using a probabilistic neural network (PNN) to analyze seismic data and well logs to identify physical attributes, describing the layers and processing of the PNN model as well as examples of preprocessing seismic data and attributes to train the PNN to accurately predict properties like porosity and hydrocarbon volume. The PNN is trained on normalized seismic attribute data and well logs then applied to the full 3D seismic volume to generate property predictions across the area.

House Sale Price Prediction

sriram30691

The document provides an overview of different machine learning algorithms used to predict house sale prices in King County, Washington using a dataset of over 21,000 house sales. Linear regression, neural networks, random forest, support vector machines, and Gaussian mixture models were applied. Neural networks with 100 hidden neurons performed best with an R-squared of 0.9142 and RMSE of 0.0015. Random forest had an R-squared of 0.825. Support vector machines achieved 73% accuracy. Gaussian mixture modeling clustered homes into three groups and achieved 49% accuracy.

C3 w1

Ajay Taneja

Enhancing the performance of Naive Bayesian Classifier using Information Gain...

Rafiul Sabbir

Two strategies for large-scale multi-label classification on the YouTube-8M d...

Dalei Li

Predicting Moscow Real Estate Prices with Azure Machine Learning

Leo Salemann

Predicting Moscow Real Estate Prices with Azure Machine Learning

Karunakar Kotha

The document summarizes experiments conducted by Team D-Hawks to predict real estate prices in Moscow using data from Kaggle competitions. It describes five different experiments that varied the features used, data cleaning techniques, and machine learning models. The winning experiment used parallel data cleaning paths and multiple boosted decision tree models to achieve the lowest root mean squared error. The team's work demonstrated that feature selection, additional data sources, and testing multiple approaches can improve price prediction accuracy.

Predicting Moscow Real Estate Prices with Azure Machine Learning

Wenfan Xu

Poster_Reseau_Neurones_Journees_2013

Pedro Lopes

The document presents research on using neural networks to predict Earth Orientation Parameters (EOP) such as UT1-TAI. Three neural network models were tested: 1) Network 1 varied the number of neurons proportionally with increasing training sample size. 2) Network 2 kept the number of neurons constant while increasing sample size. 3) Network 3 used daily training data with 2 neurons and sample sizes of 4, 10, 20, and 365 days. The goal was to minimize prediction error (RMSE) for horizons of 5-25 days by adjusting sample size and neurons. Results showed the best balance was needed between these factors, and that short-term prediction was possible within 10 days using

Policy Based reinforcement Learning for time series Anomaly detection

Kishor Datta Gupta

This document discusses a policy-based reinforcement learning approach called PTAD for time series anomaly detection. PTAD formulates anomaly detection as a Markov Decision Process and uses an asynchronous actor-critic algorithm to learn a stochastic policy. The agent takes as input current and previous time series data and actions, and outputs a decision of normal or anomalous. It is rewarded based on a confusion matrix calculation. Experimental results show PTAD achieves best performance both within and across datasets by adjusting to different behaviors. The stochastic policy allows exploring precision-recall tradeoffs. While interesting, it is not compared to neural network based techniques like autoencoders.

Introduction to Genetic algorithm and its significance in VLSI design and aut...

Centre for Electronics, Computer, Self development

This document provides an introduction to genetic algorithms and their applications in VLSI design and automation. It discusses the fundamentals of genetic algorithms including genetic representation, selection, crossover and mutation operators. Examples are provided for simple function optimization and the traveling salesman problem. The document also discusses applications of genetic algorithms for VLSI design problems such as partitioning, placement, routing, technology mapping and automatic test pattern generation. It provides details on genetic algorithm parameters and compares genetic algorithms to traditional optimization methods.

JOB SCHEDULING USING ANT COLONY OPTIMIZATION ALGORITHM

mailjkb

This document discusses using machine learning algorithms for job scheduling in a grid computing environment. It aims to minimize makespan, the total time to complete all tasks, by learning from past scheduling experiences. It proposes using ant colony optimization, where artificial ants probabilistically choose task-machine pairs to incrementally find optimal schedules. The algorithm is compared to other scheduling methods and extended to online scheduling by classifying jobs with attributes to appropriate machines. A feasibility study demonstrates classification and scheduling of test jobs using machine learning tools.

Predictive analytics

Dinakar nk

Predictive analytics uses data mining, statistical modeling and machine learning techniques to extract insights from existing data and use them to predict unknown future events. It involves identifying relationships between variables in historical data and applying patterns to unknowns. Predictive analytics is more sophisticated than analytics which has a retrospective focus on understanding trends, while predictive analytics focuses on gaining insights for decision making. Common predictive analytics techniques include regression, classification, time series forecasting, association rule mining and clustering. Ensemble methods like bagging, boosting and stacking combine multiple predictive models to improve performance.

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

VICTOR MAESTRE RAMIREZ

An improved modulation technique suitable for a three level flying capacitor ...

IJECEIAES

This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed simplified modulation technique paves the way for more straightforward and efficient control of multilevel inverters, enabling their widespread adoption and integration into modern power electronic systems. Through the amalgamation of sinusoidal pulse width modulation (SPWM) with a high-frequency square wave pulse, this controlling technique attains energy equilibrium across the coupling capacitor. The modulation scheme incorporates a simplified switching pattern and a decreased count of voltage references, thereby simplifying the control algorithm.

Similar to Classification of Grasp Patterns using sEMG

Cross-validation aggregation for forecasting

Devon Barrow

KCC2017 28APR2017

JEE HYUN PARK

Review : Prototype Mixture Models for Few-shot Semantic Segmentation

Dongmin Choi

230727_HB_JointJournalClub.pptx

Network Science Lab, The Catholic University of Korea

Grid search.pptx

AbithaSam

Using HOG Descriptors on Superpixels for Human Detection of UAV Imagery

Wai Nwe Tun

Mattar_PhD_Thesis

Marwan Mattar

PNN and inversion-B

Stig-Arne Kristoffersen

House Sale Price Prediction

sriram30691

C3 w1

Ajay Taneja

Enhancing the performance of Naive Bayesian Classifier using Information Gain...

Rafiul Sabbir

Two strategies for large-scale multi-label classification on the YouTube-8M d...

Dalei Li

Predicting Moscow Real Estate Prices with Azure Machine Learning

Leo Salemann

Predicting Moscow Real Estate Prices with Azure Machine Learning

Karunakar Kotha

Predicting Moscow Real Estate Prices with Azure Machine Learning

Wenfan Xu

Poster_Reseau_Neurones_Journees_2013

Pedro Lopes

Policy Based reinforcement Learning for time series Anomaly detection

Kishor Datta Gupta

Introduction to Genetic algorithm and its significance in VLSI design and aut...

Centre for Electronics, Computer, Self development

JOB SCHEDULING USING ANT COLONY OPTIMIZATION ALGORITHM

mailjkb

Predictive analytics

Dinakar nk

Similar to Classification of Grasp Patterns using sEMG (20)

Cross-validation aggregation for forecasting

KCC2017 28APR2017

Review : Prototype Mixture Models for Few-shot Semantic Segmentation

230727_HB_JointJournalClub.pptx

Grid search.pptx

Using HOG Descriptors on Superpixels for Human Detection of UAV Imagery

Mattar_PhD_Thesis

PNN and inversion-B

House Sale Price Prediction

C3 w1

Enhancing the performance of Naive Bayesian Classifier using Information Gain...

Two strategies for large-scale multi-label classification on the YouTube-8M d...

Predicting Moscow Real Estate Prices with Azure Machine Learning

Poster_Reseau_Neurones_Journees_2013

Policy Based reinforcement Learning for time series Anomaly detection

Introduction to Genetic algorithm and its significance in VLSI design and aut...

JOB SCHEDULING USING ANT COLONY OPTIMIZATION ALGORITHM

Predictive analytics

Recently uploaded

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

VICTOR MAESTRE RAMIREZ

An improved modulation technique suitable for a three level flying capacitor ...

IJECEIAES

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Sinan KOZAK

Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.

Comparative analysis between traditional aquaponics and reconstructed aquapon...

bijceesjournal

The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.

4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf

Gino153088

Material for memory and display system h

gowrishankartb2005

Curve Fitting in Numerical Methods Regression

Nada Hikmah

Properties Railway Sleepers and Test.pptx

MDSABBIROJJAMANPAYEL

Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

shadow0702a

This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL. The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process. The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging. It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal. Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages. Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.

Software Quality Assurance-se412-v11.ppt

TaghreedAltamimi

People as resource Grade IX.pdf minimala

riddhimaagrawal986

Seminar on Distillation study-mafia.pptx

Madan Karki

Engineering Drawings Lecture Detail Drawings 2014.pdf

abbyasa1014

Generative AI leverages algorithms to create various forms of content

Hitesh Mohapatra

2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf

Yasser Mahgoub

CEC 352 - SATELLITE COMMUNICATION UNIT 1

PKavitha10

Mechanical Engineering on AAI Summer Training Report-003.pdf

21UME003TUSHARDEB

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

IJECEIAES

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network

Applications of artificial Intelligence in Mechanical Engineering.pdf

Atif Razi

Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI. AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.

Computational Engineering IITH Presentation

co23btech11018

Recently uploaded (20)

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

An improved modulation technique suitable for a three level flying capacitor ...

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Comparative analysis between traditional aquaponics and reconstructed aquapon...

4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf

Material for memory and display system h

Curve Fitting in Numerical Methods Regression

Properties Railway Sleepers and Test.pptx

Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

Software Quality Assurance-se412-v11.ppt

People as resource Grade IX.pdf minimala

Seminar on Distillation study-mafia.pptx

Engineering Drawings Lecture Detail Drawings 2014.pdf

Generative AI leverages algorithms to create various forms of content

2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf

CEC 352 - SATELLITE COMMUNICATION UNIT 1

Mechanical Engineering on AAI Summer Training Report-003.pdf

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

Applications of artificial Intelligence in Mechanical Engineering.pdf

Computational Engineering IITH Presentation

Classification of Grasp Patterns using sEMG

1. Classification of Grasp Patterns using sEMG Priyanka R. Reddy

2. Motivation • Build a classification model that identifies spherical and tip grasps from surface electromyography (sEMG) data

3. Types of Grasps Spherical (Power grip) Tip (Precision grip) Image: https://www.studyblue.com/notes/note/n/grasps- updated-for-human-occ/deck/4455956 Image: https://www.studyblue.com/notes/note/n/grasps- updated-for-human-occ/deck/4455956

4. Dataset • Healthy, male subject performs each grasp 100 times daily for three days • 600 instances (tip and spherical grasp data combined) • Dataset was originally band-passed filtered to eliminate line artifacts Reference: C. Sapsanis. 'Recognition of basic hand movements using electromyography'. 2013.

5. Building a Classifier 1. Specify network model 2. Data pre-processing and Feature Extraction 3. Create Training and Test Sets 4. Parameterize the network 5. Validate and query model results

6. Hidden Markov Model • Simplest form of a Dynamic Bayesian Network • Utilizes local conditional probability densities to compactly specify a joint distribution • Latent variables are discrete random variables • Observed variables are continuous random variables     N n N n nnnnNN goPggPgPggooP 2 1 1111 )/()]/()[(),...,,,...,(

7. Model Variables and Feature Extraction Feature Extraction: 𝐼𝐸𝑀𝐺 = 𝑛=1 𝑁 𝑥 𝑛 used for observations denoted 𝑂 𝑛 in HMM 𝐺 𝑛 is a binary random variable taking values in the set {0,1} where 0 – spherical grasp 1 – tip grasp Reference: A. Phinyomark, C. Limsakul, and P. Phukpataranont. “A Novel Feature Extraction for Robust EMG Pattern Recognition.” Journal of Computing. Volume 1. Issue 1, December 2009.

8. Training and Test sets • Randomly sampled dataset • 90 percent of data for training • Remaining 10 percent used for testing

9. Parameterization • Goal: fit the joint probability distribution to the data, i.e. find the parameters • Bayesian Logic (BLOG) computes posterior distributions based on specified priors and observation values using sampling algorithms • Particle filter chosen to run the HMM

10. Computing Posterior Distributions Specified priors in BLOG: P(𝐺1) – probability mass function (pmf) 𝑃(𝐺 𝑛|𝐺 𝑛−1) – conditional probability table 𝑃(𝑂𝑛|𝐺 𝑛) – Gaussian distribution = 1 𝜎 2𝜋 𝑒− (𝑥−𝜇 𝑖)2 2𝜎2 , i = 0, 1 𝑃(𝐺 𝑛|𝑂 𝑛) ∝ 𝑃(𝑂𝑛|𝐺 𝑛) 𝑃(𝐺 𝑛 )

11. Results Overall Model Accuracy: 73.3% Grasp Spherical Tip Sensitivity 91% 58% False Positive 42% 1% False Negative 1% 42% Specificity 58% 91%

12. Conclusion • Current model predicts spherical grasps more accurately than tip grasps • Beneficial for picking up large objects as spherical grasps are more effective for this task as compared to tip grasps • Improvement can be made to increase accuracy – 1. Include additional features to feature vector and try various combinations of vectors – 2. Stratify the random sample when creating training and test sets to ensure data from both groups is represented

Classification of Grasp Patterns using sEMG

Recommended

Recommended

More Related Content

Similar to Classification of Grasp Patterns using sEMG

Similar to Classification of Grasp Patterns using sEMG (20)

Recently uploaded

Recently uploaded (20)

Classification of Grasp Patterns using sEMG