Gesture imitation using machine learning techniques

Itauma, Itauma Isong; Kivrak, Hasan; Köse, Hatice

doi:10.1109/siu.2012.6204822

Cited by 17 publications

(11 citation statements)

References 8 publications

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“…Many papers presented in the literature in the last couple of years have used Kinect sensors [5][6][7][8][9][10][11][12][13] and they select several features and different classification methods to build gesture models in several applicative contexts. This section will overview those studies mainly focused on gesture recognition approaches in the HRI context [14] and (if not explicitly expressed) which use the Kinect camera as a RGB-D sensor. The ability of the OpenNI framework to easily provide the position and segmentation of the hand has stimulated many approaches to the recognition of hand gestures [8,15,16].…”

Section: Related Workmentioning

confidence: 99%

A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface

Cicirelli

Attolico

Guaragnella

et al. 2015

International Journal of Advanced Robotic Systems

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In this paper, we present a gesture recognition system for the development of a human-robot interaction (HRI) interface. Kinect cameras and the OpenNI framework are used to obtain real-time tracking of a human skeleton. Ten different gestures, performed by different persons, are defined. Quaternions of joint angles are first used as robust and significant features. Next, neural network (NN) classifiers are trained to recognize the different gestures. This work deals with different challenging tasks, such as the real-time implementation of a gesture recognition system and the temporal resolution of gestures. The HRI interface developed in this work includes three Kinect cameras placed at different locations in an indoor environment and an autonomous mobile robot that can be remotely controlled by one operator standing in front of one of the Kinects. Moreover, the system is supplied with a people reidentification module which guarantees that only one person at a time has control of the robot. The system's performance is first validated offline, and then online experiments are carried out, proving the real-time operation of the system as required by a HRI interface.

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Section: Related Workmentioning

confidence: 99%

A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface

Cicirelli

Attolico

Guaragnella

et al. 2015

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…In many applications, the NAO robot and Kinect were employed together to find the medical and social requirements of a person with mobility difficulties, older adults, or children [36][37][38][39][40][41][42][43]. In [36], an inverse Kinematics model was used on the learning stage by demonstration of NAO for teleoperation.…”

Section: Introductionmentioning

confidence: 99%

“…In [38], an NAO controlled by Kinect sensor was used to cure patients subject to physical treatments. A learning method was introduced for the upper-body actions of children with hearing disabilities by using the NAO robot and the Kinect in [39]. In [40], the NAO robot imitated whole-body motions of human by a motion capture system consisting of inertial sensors attached to the body.…”

Section: Introductionmentioning

confidence: 99%

Gesture imitation and recognition using Kinect sensor and extreme learning machines

Yavşan

Uçar

2016

Measurement

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This study presents a framework that recognizes and imitates human upper-body motions in real time. The framework consists of two parts. In the first part, a transformation algorithm is applied to 3D human motion data captured by a Kinect. The data are then converted into the robot's joint angles by the algorithm. The human upper-body motions are successfully imitated by the NAO humanoid robot in real time. In the second part, the human action recognition algorithm is implemented for upper-body gestures. A human action dataset is also created for the upper-body movements. Each action is performed 10 times by twenty-four users. The collected joint angles are divided into six action classes. Extreme Learning Machines (ELMs) are used to classify the human actions. Additionally, the Feed-Forward Neural Networks (FNNs) and K-Nearest Neighbor (K-NN) classifiers are used for comparison. According to the comparative results, ELMs produce a good human action recognition performance.

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“…İleri beslemeli tek gizli katmana sahip bir YSA ve AÖM kullanılarak hareket tanıma başarımları karşılaştırılmış ve sonuçlar, süre ve tanıma kararlılığı olarak verilmiştir. NAO robot ve Kinect algılayıcının birlikte kullanıldığı çalışmalar gün geçtikçe artmaktadır [10][11][12][13][14][15]. Genellikle özel ihtiyacı bulunan bireylerin tıbbi ve sosyal gereksinimlerinin karşılanmasına yönelik çalışmalarda bu ikiliye rastlanmaktadır [11].…”

Section: Introductionunclassified

“…Bu çalışmaya benzer bir şekilde NAO robotun kullanıldığı ve ters kinematik modelin uygulandığı bir diğer çalışma, Mota ve arkadaşları tarafından gerçekleştirilmiştir [14]. NAO ve Kinect ikilisinin kullanıldığı devam eden bir projenin ürünü olarak Itauma, Kivrak ve Kose tarafından işitme engelli çocuklara işaret dilinin öğretilmesi amacıyla farklı öğrenme tekniklerinin tanıtıldığı bir çalışma yapılmıştır [15].…”

Section: Introductionunclassified

Teaching human gestures to humanoid robots by using Kinect sensor

Yavşan

Uçar

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Bu çalışmada, insan hareketlerinin tanınması ve insansı bir robot üzerinde gerçekleştirilmesine yönelik yeni bir algoritma geliştirilmiştir. İki kısımdan oluşan çalışmanın birinci kısmında, eş zamanlı bir taklit sistemi sunulmaktadır. Xbox 360 Kinect algılayıcıdan elde edilen üç boyutlu iskelet eklem pozisyonları, bir dönüşüm algoritması sayesinde robotun kol eklem açılarına dönüştürülmekte ve sonrasında bu açılar, NAO insansı robota aktarılmaktadır. Bu kısmın sonunda, insan üst vücut hareketleri, NAO robot tarafından eş zamanlı olarak başarılı bir şekilde taklit edilmiştir. Çalışmanın ikinci kısmında, insan hareketlerinin tanınması için bir algoritma oluşturulmuştur. İnsan hareketlerini sınıflandırmak için Aşırı Öğrenme Makinaları (AÖM) ve geriye yayılım algoritması ile eğitilen ileri beslemeli Yapay Sinir Ağları (YSA) kullanılmıştır. Karşılaştırmalı sonuçlara göre, AÖM ile daha iyi bir tanıma başarımı elde edilmiştir. Anahtar Kelimeler -İnsan hareketlerinin tanınması; NAO insansı robot; Xbox 360 Kinect.Abstract-In this study, a novel algorithm is developed to recognize human actions and reproduce human actions on a humanoid robot. The study consists of two parts. In the first part, the real time human imitation system is realized. The three dimensional skeleton joint positions obtained from Xbox 360 Kinect. These positions are transformed to joint angles of robot arms via a transformation algorithm and these angles are transferred to NAO robot. The human upper body movements are finally successfully imitated by NAO robot in real time. In the second part, the algorithm is generated for the recognition of human actions. Extreme Learning Machines (ELMs) and the Feed Forward Neural Networks (FNNs) with back propagation algorithm are used to classify actions. According to the comparative results, ELMs produce a better recognition performance.

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Gesture imitation using machine learning techniques

Cited by 17 publications

References 8 publications

A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface

A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface

Gesture imitation and recognition using Kinect sensor and extreme learning machines

Teaching human gestures to humanoid robots by using Kinect sensor

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