A Low Cost Solution of Hand Gesture Recognition Using a Three-Dimensional Radar Array

2019 ◽  
Vol E102.B (2) ◽  
pp. 233-240
Author(s):  
Shengchang LAN ◽  
Zonglong HE ◽  
Weichu CHEN ◽  
Kai YAO
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Three Dimensional ◽  
Hand Gesture Recognition ◽  
Hand Gesture

scholarly journals A PointNet-Based Solution for 3D Hand Gesture Recognition

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3226
Author(s):  
Radu Mirsu ◽  
Georgiana Simion ◽  
Catalin Daniel Caleanu ◽  
Ioana Monica Pop-Calimanu
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Deep Neural Network ◽  
Three Dimensional ◽  
Time Of Flight ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Hand Gesture ◽  
Volume Of Interest ◽  
Hand Segmentation

Gesture recognition is an intensively researched area for several reasons. One of the most important reasons is because of this technology’s numerous application in various domains (e.g., robotics, games, medicine, automotive, etc.) Additionally, the introduction of three-dimensional (3D) image acquisition techniques (e.g., stereovision, projected-light, time-of-flight, etc.) overcomes the limitations of traditional two-dimensional (2D) approaches. Combined with the larger availability of 3D sensors (e.g., Microsoft Kinect, Intel RealSense, photonic mixer device (PMD), CamCube, etc.), recent interest in this domain has sparked. Moreover, in many computer vision tasks, the traditional statistic top approaches were outperformed by deep neural network-based solutions. In view of these considerations, we proposed a deep neural network solution by employing PointNet architecture for the problem of hand gesture recognition using depth data produced by a time of flight (ToF) sensor. We created a custom hand gesture dataset, then proposed a multistage hand segmentation by designing filtering, clustering, and finding the hand in the volume of interest and hand-forearm segmentation. For comparison purpose, two equivalent datasets were tested: a 3D point cloud dataset and a 2D image dataset, both obtained from the same stream. Besides the advantages of the 3D technology, the accuracy of the 3D method using PointNet is proven to outperform the 2D method in all circumstances, even the 2D method that employs a deep neural network.

scholarly journals Human Actions and Hand Gesture Recognition with Deep Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1250-1253
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Gesture Recognition ◽  
Human Activities ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Characteristic Features

Over recent times, deep learning has been challenged extensively to automatically read and interpret characteristic features from large volumes of data. Human Action Recognition (HAR) has been experimented with variety of techniques like wearable devices, mobile devices etc., but they can cause unnecessary discomfort to people especially elderly and child. Since it is very vital to monitor the movements of elderly and children in unattended scenarios, thus, HAR is focused. A smart human action recognition method to automatically identify the human activities from skeletal joint motions and combines the competencies are focused. We can also intimate the near ones about the status of the people. Also, it is a low-cost method and has high accuracy. Thus, this provides a way to help the senior citizens and children from any kind of mishaps and health issues. Hand gesture recognition is also discussed along with human activities using deep learning.

scholarly journals Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 564 ◽  
Author(s):  
Shahzad Ahmed ◽  
Sung Ho Cho
Keyword(s):  
Gesture Recognition ◽  
Human Subjects ◽  
Three Dimensional ◽  
Hand Gesture Recognition ◽  
Ultra Wideband ◽  
Hand Gesture ◽  
Natural Mode ◽  
Hand Gestures ◽  
Daily Lives ◽  
Deep Learning Network

The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.

scholarly journals A COLORED FINGER TIP-BASED TRACKING METHOD FOR CONTINUOUS HAND GESTURE RECOGNITION

2014 ◽  
pp. 236-240
Author(s):  
DHARANI MAZUMDAR ◽  
ANJAN KUMAR TALUKDAR ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Body Parts ◽  
Dynamic Background ◽  
Lighting Condition ◽  
Background Removal ◽  
Hand Segmentation ◽  
Finger Tip

Hand gesture recognition system can be used for human-computer interaction (HCI). Proper hand segmentation from the background and other body parts of the video is the primary requirement for the design of a hand-gesture based application. These video frames can be captured from a low cost webcam (camera) for use in a vision based gesture recognition technique. This paper discusses about the continuous hand gesture recognition. The aim of this paper is to report a robust and efficient hand segmentation algorithm where a new method, wearing glove on the hand is utilized. After that a new idea called “Finger-Pen”, is developed by segmenting only one finger from the hand for proper tracking. In this technique only a finger tip is segmented in spite of the full hand part. Hence this technique allows the hand (excepting the segmented finger tip) to move freely during the tracking time also. Problems such as skin colour detection, complexity from large numbers of people in front of the camera, complex background removal and variable lighting condition are found to be efficiently handled by the system. Noise present in the segmented image due to dynamic background can be removed with the help of this adaptive technique which is found to be effective for the application conceived.

Development of Robotic Arm Through Hand Gesture Recognition Using Leap Motion Sensor

2020 ◽  
Vol 17 (4) ◽  
pp. 1889-1893
Author(s):  
T. Archana ◽  
Srigitha S. Nath ◽  
S. Praveenkumar
Keyword(s):  
Gesture Recognition ◽  
Control Strategy ◽  
Low Cost ◽  
Hand Gesture Recognition ◽  
Robotic Arm ◽  
Hand Gesture ◽  
Motion Sensor ◽  
Leap Motion ◽  
Control Techniques ◽  
Natural Movement

The objective of this paper has been the development of a prototype of articulated Robotic arm and implementation of a control strategy for gesture recognition through (Leap motion sensor), by means the natural movement of the fore-arm and hand. The series of advances relative to the control techniques have caused that the robotics it has also introduced as an educational and complement in obligatory basic teachings. To develop and to control Robotic elements locally or remotely, it has always proven to be a clear example of additional motivation. The prototype developed has exceeded the initial expectations and at low cost.

scholarly journals Hand Gesture Recognition in Automotive Human–Machine Interaction Using Depth Cameras

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 59 ◽  
Author(s):  
Nico Zengeler ◽  
Thomas Kopinski ◽  
Uwe Handmann
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Hand Gesture Recognition ◽  
Sensor Data ◽  
Hand Gesture ◽  
Learning Approaches ◽  
Human Machine Interaction ◽  
Depth Cameras ◽  
Learning Framework

In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.

Low-cost assistive device for hand gesture recognition using sEMG (Erratum)

2016 ◽  
Author(s):  
Dávid Cymbalák ◽  
Slavomír Kardoš ◽  
Peter Feciľak ◽  
František Jakab ◽  
Andrej Mak ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Hand Gesture Recognition ◽  
Assistive Device ◽  
Hand Gesture
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