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Activity Recognition Using Transfer learning

SENSORS AND MATERIALS 2017年第7期29卷 897-904页

作者： Chen, Wen-Hui Cho, Po-Chuan Jiang, Yong-Lin Natl Taipei Univ Technol Grad Inst Automat Technol 1 Sect 3Zhongxiao E Rd Taipei Taiwan

the technology for human activity recognition has become an active research topic in recent years as it has many potential applications, such as surveillance systems, healthcare systems, and human-computer interaction. In the research of activity recognition, supervised machine learning approaches have been widely used for activity recognition. However, the cost of collecting labeled sensor data in new environments is high. Furthermore, these methods do not work well in a cross domain environment using conventional machine learning approaches. In this study, we proposed a transfer learning framework based on principal component analysis (PCA) transformation, Gale Shapley similarity measurement, and Jensen Shannon divergence (JSD) feature mapping. Transfer learning aims to apply new information learned from the source domain to the target domain. the experimental results showed that the proposed approach performs better than the approach merely learned in the source environment.

关键词： activity recognition machine learning transfer learning smart homes

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Smile detection using hybrid face representation

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JOURNAL OF AMBIENT INTELLIGENCE AND humanIZED COMPUTING 2016年第3期7卷 415-426页

作者： Arigbabu, Olasimbo Ayodeji Mahmood, Saif Ahmad, Sharifah Mumtazah Syed Arigbabu, Abayomi A. Univ Putra Malaysia Fac Engn Serdang 43400 Selangor Malaysia Tai Solarin Univ Educ Dept Math PMB 2118 Ijebu Nigeria

Smile detection has attracted considerable amount of research interests in the domain of computer vision. It possesses several potential applications in gaming, human-to-computer and human-to-human interaction. this paper investigates the challenging problem of smile detection from face images acquired under unconstrained conditions. First, a locally weighted multiblock shape-texture descriptor is proposed to extract detailed local and global information from faces with diverse variations such as orientation, illumination, pose, and occlusion. the proposed technique combines the robustness of pyramid histogram of oriented gradient and local binary pattern for image feature representation using an adaptive local weight assignment. By locally weighting the descriptors from very dense patches of the image, we induce discriminating local spatial context to the distribution of the descriptions from the face image. Second, in order to minimize redundancy and extract the most relevant facial information from the feature vectors, a correlation based filter feature selection approach is adopted. Finally, kernel based classifiers such as support vector machine and kernel extreme learning machine are utilized for performing classification. Based on our findings, the proposed framework provides very competitive detection rate to related approaches that have exploited image alignment as an important stage for improving performance of smile detection.

关键词： Smile detection Unconstrained face images Local feature descriptors Feature selection Kernel extreme learning machine Support vector machine

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A Review of EEG Signal Classifier based on Deep learning

A Review of EEG Signal Classifier based on Deep Learning

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ISCC 2017-the 5th international conference on Information Science and Cloud Computing

作者： Yao Lu Huiping Jiang Wenqiang Liu Brain Cognitive Computing Lab School of Information Engineering Minzu University of China

Electroencephalogram(EEG) signal recognition is an active research topic in the field of artificial intelligence and has been gaining extensive attention and engineering *** technology is an important basis of human computer interaction and many other fields. the deep learning theory has made remarkable achievements on feature extraction and gradually extended to the time sequences of EEG research. this paper reviews the traditional feature extraction of EEG recognition and discuss the traditional classification methods of EEG recognition such as linear discriminant analysis(LDA), support vector machine(SVM) and long short time memory(LSTM). Finally, this paper summarizes advantages and disadvantages of these methods. through the feature extraction by the wavelet transform and LSTM classification, it has achieved 98% accuracy and verified that LSTM is suitable for EEG signal with time sequence feature.

关键词： domain A Review of EEG Signal Classifier based on Deep learning

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Digital Game for Teaching and learning: An Analysis of Usability and Experience of Educational Games 5th

Digital Game for Teaching and Learning: An Analysis of Usabi...

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5th international conference on Design, User Experience, and Usability (DUXU) held as part of 18th international conference on human-computer interaction (HCI international)

作者： Raffaele, Rennan Carvalho, Breno Lins, Anthony Marques, Luiz Soares, Marcelo Marcio Univ Beira Interior Covilha Portugal Catholica Univ Pernambuco Recife PE Brazil Univ Fed Pernambuco Recife PE Brazil

ISBN: (纸本)9783319403557;9783319403540

Desbravadores is a 3D game, available for web, which is about History. the game is in first person, with focus on survivor, where the player is a time traveler and needs to save old artifacts, which was lost in our history. To achieve this goal the team proposed an artifact whose interface simulates a time machine, able to take students/players to meaningful moments of different historical periods, immersing it in a realistic environment, initially in 3D and future in "virtual reality". Both are educational games for awake the interest of Brazilian students and be an interactive tool for teachers in classroom. this study set out to investigate the gameplay and usability of these two games, grounded on the concepts set out by Huizinga, Preece, Rogers and Sharp, and by observation the interactions that users engaged on laboratory tests.

关键词： Educational games Usability and gameplay User's experience interaction Desbravadores Fun Playfulness

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Principles and experimentations of self-organizing embedded agents allowing learning from demonstration in ambient robotic 6th

Principles and experimentations of self-organizing embedded ...

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6th international conference on Ambient Systems, Networks and Technologies (ANT) / 5th international conference on Sustainable Energy Information Technology (SEIT)

作者： Verstaevel, Nicolas Regis, Christine Gleizes, Marie-Pierre Robert, Fabrice Univ Toulouse 3 IRIT F-31062 Toulouse France Sogeti High Tech F-31300 Toulouse France

Ambient systems are populated by many heterogeneous devices to provide adequate services to its users. the adaptation of an ambient system to the specific needs of its users is a challenging task. Because human-system interaction has to be as natural as possible, we propose an approach based on learning from Demonstration (LfD). However, using LfD in ambient systems needs adaptivity of the learning technique. We present ALEX, a multi-agent system able to dynamically learn and reuse contexts from demonstrations performed by a tutor. Results of experiments performed on both a real and a virtual robot show interesting properties of our technology for ambient applications. (C) 2015 the Authors. Published by Elsevier B.V.

关键词： Adaptive agents Cooperative multi-agents systems Robotic in ambients systems machine learning Context-Awareness

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A Hybrid Bioinspired Algorithm for Facial Emotion Recognition Using CSO-GA-PSO-SVM 5

A Hybrid Bioinspired Algorithm for Facial Emotion Recognitio...

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5th international conference on Communication Systems and Network Technologies (CSNT)

作者： Vivek, T. V. Guddeti, Ram Mohana Reddy Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

ISBN: (纸本)9781479917976

human-computer interaction gets more natural when the machine can detect human emotions faster and accurate. A lot of research is being carried out in the field of affective computing in order to improve the accuracy with speed. Bio-inspired algorithms for feature extraction and classification stages, has improved accuracy and speed further. In this paper, we propose a hybrid algorithm using CSO (Cat Swarm Optimization) with PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) for emotion recognition (ER). this bioinspired algorithm in conjunction with the support vector machine (SVM) will find an optimal feature set from a bigger set. Results from CK+ (Cohn Kanade) [1] dataset demonstrate that our proposed method using CSO-GA-PSOSVM outperforms Emotion Recognition System with CSOSVM by 10.5% in accuracy. this paper also proposes a new E-learning [2] system to demonstrate its effectiveness and efficiency in real-time scenario. the proposed algorithm is applied over the facial characteristics captured from students in teaching-learning environment. the optimized feature vector obtained is passed to the SVM classifier for classification. Experimental results yield 99% classification accuracy in a person dependent mode with six basic emotions namely Happy, Sad, Anger, Disgust, Surprise and Neutral.

关键词： Emotion Recognition Bioinspired Algorithms Cat Swarm Optimization Genetic Algorithm Particle Swarm Optimization Support Vector machines Hybrid Bioinspired Algorithms E-learning

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A Study on Analysis of Bio-signals for Basic Emotions Classification Recognition using machine learning Algorithms

A Study on Analysis of Bio-signals for Basic Emotions Classi...

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5th international conference on Information Science and Applications (ICISA)

作者： Jang, Eun-Hye Park, Byoung-Jun Kim, Sang-Hyeob Eum, Youngji Sohn, Jin-Hun Elect & Telecommun Res Inst Biohlth IT Convergence Technol Res Dept Taejon 305606 South Korea Elect & Telecommun Res Inst Future Technol Res Dept Taejon 305606 South Korea Chungnam Natl Univ Brain Res Inst Dept Psychol Taejon South Korea

ISBN: (纸本)9781479944415

the most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. the aim of this study is to classify six emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multi-channel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using five algorithms, linear discriminant analysis, Naive Bayes, classification and regression tree, self-organization map and support vector machine. the used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 42.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naive Bayes and linear discriminant analysis were highest (53.9%, 52.7%) and was lowest by support vector machine (39.2%). this means that Naive Bayes is the best emotion recognition algorithm for basic emotions. To apply to real system, we have to discuss in the view point of testing and this means that it needs to apply various methodologies for the accuracy improvement of emotion recognition in the future analysis.

关键词： emotion bio-signal machine learning algorithm

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Hand Gesture Recognition for human computer interaction: A Comparative Study of Different Image Features 5

Hand Gesture Recognition for Human Computer Interaction: A C...

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5th international conference on Agents and Artificial Intelligence (ICAART)

作者： Trigueiros, Paulo Ribeiro, Fernando Reis, Luis Paulo Univ Minho Dept Elect Ind P-4800058 Guimaraes Portugal Univ Minho DSI EEUM Escola Engn P-4800058 Guimaraes Portugal

ISBN: (纸本)9783662444405;9783662444399

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. this is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. the extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91 % and 90,1 % respectively obtained with a Neural Network classifier. these to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

关键词： Hand gesture recognition machine vision Hand features Hog (Histogram of oriented Gradients) Fourier descriptors Centroid distance Radial signature Shi-Tomasi corner detection

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Research on digital etiquette based on human-computer interaction

Research on digital etiquette based on human-computer intera...

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2013 5th international conference on Intelligent human-machine Systems and Cybernetics, IHMSC 2013

作者： Chai, Chunlei Cen, Fei Sun, Lingyun Modern Industrial Design Institute Zhejiang University Hangzhou China

ISBN: (纸本)9780769550114

Different etiquette of software could trigger different attitudes and behaviors of users. this paper conducted a series of word-learning studies in which users either received polite feedback offering compliments and encouragements, or impolite feedback belittling users. through the analysis on the results of e-learning experiment and post-test questionnaires, it was found that the polite feedback could yield better learning outcomes and greater improvement on satisfaction. © 2013 IEEE.

关键词： human computer interaction

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Gesture recognition by using kinect skeleton tracking system

Gesture recognition by using kinect skeleton tracking system

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2013 5th international conference on Intelligent human-machine Systems and Cybernetics, IHMSC 2013

作者： Wang, Baoliang Chen, Zeyu Chen, Jing E-Learning Lab Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9780769550114

With the rapid development of computer technology, these years has witnessed the rapid improvement of the approaches of human computer interaction. How to make the process more natural to the users has become a hot research topic. Nowadays, more and more researchers pay attention to gesture recognition, especially when kinect is invented by Microsoft. Because it is an ordinary and natural way in which we interact with each other. this paper put forward a novel method in which we use an innovative way to abstract and notate the features of two-hand gestures and build Coupled Hidden Markov Models for two-hand gesture recognition. this method can recognize two-hand gestures. Our experiment results demonstrate that it is an effective approach for two-hand gestures recognition. © 2013 IEEE.

关键词： Gesture recognition

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