Human gesture recognition performance evaluation for service robots

2017 ◽  
Author(s):  
Mi-Young Cho ◽  
Young-Sook Jeong
Keyword(s):  
Performance Evaluation ◽  
Gesture Recognition ◽  
Recognition Performance ◽  
Service Robots

A New Approach for Face Recognition Performance Evaluation for Robot Using LED Monitor

2014 ◽  
Vol 548-549 ◽  
pp. 939-942 ◽  
Author(s):  
Mi Young Cho ◽  
Young Sook Jeong ◽  
Byung Tae Chun
Keyword(s):  
Performance Evaluation ◽  
Face Recognition ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Test Methods ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Service Robots ◽  
Face Detection And Recognition ◽  
Performance Evaluation Method

With the increasing of service robots, human-robot interaction for natural communication between user and robot is becoming more and more important. Especially, face recognition is a key issue of HRI. Even though robots mainly use face detection and recognition to provide various services, it is still difficult to guarantee of performance due to insufficient test methods in point of view robot. So, we propose a new performance evaluation method for robot using LED monitor.

scholarly journals Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1007
Author(s):  
Chi Xu ◽  
Yunkai Jiang ◽  
Jun Zhou ◽  
Yi Liu
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition Task ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Estimation Task ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

scholarly journals Performance Evaluation of Support Vector Machine Algorithm for Human Gesture Recognition

2020 ◽  
pp. 204-210
Author(s):  
Vina Ayumi
Keyword(s):  
Support Vector Machine ◽  
Performance Evaluation ◽  
Gesture Recognition ◽  
Research Area ◽  
Human Motion ◽  
Support Vector ◽  
Body Movements ◽  
Average Accuracy ◽  
Svm Algorithm ◽  
Human Motion Recognition

Research on human motion gesture recognition has been widely used for several technological devices to support monitoring of human-computer interaction, elderly people and so forth. This research area can be observed by conducting experiments for several body movements, such as hand movements, or body movements as a whole. Many methods have been used for human motion gesture recognition in previous studies. This paper attempted to collect data of performance evaluation of support vector machine algorithms for human motion recognition. We developed research methodology that is adapted PRISMA. This methodology is consisted of four main steps for reviewing scientific articles, including identification, screening, eligibility and inclusion criteria. After we obtained result of systematic literature review. We also conducted pilot study of SVM implementation for human gesture recognition. Based on the previous study result, the accuracy performance of vector machine algorithms for body gesture dataset is between 82.88% - 99.92% and hand gesture dataset 88.24% - 95.42%. Based on our pilot experiment, recognition accuracy with the SVM algorithm for human gesture recognition achieved 94,50% (average) accuracy.

scholarly journals putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3548 ◽  
Author(s):  
Piotr Kaczmarek ◽  
Tomasz Mańkowski ◽  
Jakub Tomczyński
Keyword(s):  
Gesture Recognition ◽  
Motion Tracking ◽  
Recognition Performance ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Classification Methods ◽  
Hand Gestures ◽  
Semg Signal ◽  
Feature Sets

In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject’s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin’s and Du’s feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.

Sign in / Sign up

Export Citation Format

Share Document