Cloud-assisted hugtive robot for affective interaction

Owing to the quickening pace and increasing pressure of daily life, people pay more and more attention to life in spiritual level. However, the time for meeting relatives or friends in person is quite short, therefore, it is more and more important for remote emotional communication (i.e., emotional perception and interaction) between users. The existing remote interaction systems mainly pay attention to voice and video communication, and it is difficult to meet the emotional needs of people. How to realize remote emotional communication between different people still faces challenge. In order to cope with this challenge, cloud-assisted hugtive robot (CH-Robot) system is designed in this paper. More specifically, firstly a new-type hugtive robot is designed. Secondly data collected by smart phone and smart clothing are adopted to judge emotional status of user, then emotional communication between users is realized through CH-Robot. Finally, a specific application scene is presented where a mother who is on business in other places comforts her child at home, thus to verify feasibility and effectiveness of the system.

[1]  Sheng Chen,et al.  Social-aware D2D communications: qualitative insights and quantitative analysis , 2014, IEEE Communications Magazine.

[2]  Min Chen,et al.  Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring , 2016, Mobile Networks and Applications.

[3]  Ryohei Nakatsu,et al.  An affective telepresence system using smartphone high level sensing and intelligent behavior generation , 2014, HAI.

[4]  Min Chen,et al.  Software-Defined Network Function Virtualization: A Survey , 2015, IEEE Access.

[5]  Victor C. M. Leung,et al.  EMC: Emotion-aware mobile cloud computing in 5G , 2015, IEEE Network.

[6]  M. Shamim Hossain,et al.  Relational User Attribute Inference in Social Media , 2015, IEEE Transactions on Multimedia.

[7]  Xuemin Shen,et al.  Mobile crowdsourcing [Editor's note] , 2015, IEEE Network.

[8]  Yonggang Wen,et al.  On the Cost–QoE Tradeoff for Cloud-Based Video Streaming Under Amazon EC2's Pricing Models , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Jian He,et al.  iCloudAccess: Cost-Effective Streaming of Video Games From the Cloud With Low Latency , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Min Chen,et al.  Demo: LIVES: Learning through Interactive Video and Emotion-aware System , 2015, MobiHoc.

[11]  Zoraida Callejas Carrión,et al.  Sentiment Analysis: From Opinion Mining to Human-Agent Interaction , 2016, IEEE Transactions on Affective Computing.

[12]  M. Shamim Hossain,et al.  Audio-visual emotion recognition using multi-directional regression and Ridgelet transform , 2016, Journal on Multimodal User Interfaces.

[13]  Tarik Taleb,et al.  On Service Resilience in Cloud-Native 5G Mobile Systems , 2016, IEEE Journal on Selected Areas in Communications.

[14]  Min Chen,et al.  Toward Cost-Effective Mobile Video Streaming via Smart Cache With Adaptive Thresholding , 2015, IEEE Transactions on Broadcasting.

[15]  Min Chen,et al.  Cloud-based Wireless Network: Virtualized, Reconfigurable, Smart Wireless Network to Enable 5G Technologies , 2015, Mob. Networks Appl..

[16]  Depeng Jin,et al.  Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures , 2016, IEEE Transactions on Vehicular Technology.

[17]  Min Chen,et al.  Coping With Emerging Mobile Social Media Applications Through Dynamic Service Function Chaining , 2016, IEEE Transactions on Wireless Communications.

[18]  Yunhao Liu,et al.  FLIGHT: Clock Calibration and Context Recognition Using Fluorescent Lighting , 2014, IEEE Transactions on Mobile Computing.

[19]  Xiaohu Ge,et al.  On inter-cell interference factor in the uplinks of multicell planar networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[20]  Min Chen,et al.  Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks , 2016, Sensors.

[21]  Ioannis Patras,et al.  Fusion of facial expressions and EEG for implicit affective tagging , 2013, Image Vis. Comput..

[22]  Sheng Chen,et al.  Multiple Mobile Data Offloading Through Disruption Tolerant Networks , 2014, IEEE Transactions on Mobile Computing.

[23]  M. Shamim Hossain,et al.  Audio-Visual Emotion Recognition Using Big Data Towards 5G , 2016, Mob. Networks Appl..

[24]  Giancarlo Fortino,et al.  Automatic Methods for the Detection of Accelerative Cardiac Defense Response , 2016, IEEE Transactions on Affective Computing.

[25]  Athanasios V. Vasilakos,et al.  Cloud-assisted body area networks: state-of-the-art and future challenges , 2014, Wirel. Networks.

[26]  Enzo Pasquale Scilingo,et al.  Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability , 2015, IEEE Transactions on Affective Computing.

[27]  Zhu Han,et al.  Optimal Base Station Scheduling for Device-to-Device Communication Underlaying Cellular Networks , 2016, IEEE Journal on Selected Areas in Communications.

[28]  Christian Jutten,et al.  Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects , 2015, Proceedings of the IEEE.

[29]  Ratul Mahajan,et al.  Proceedings of the 2012 ACM conference on Internet measurement conference , 2012 .

[30]  Kai-Tai Song,et al.  Robotic Emotional Expression Generation Based on Mood Transition and Personality Model , 2013, IEEE Transactions on Cybernetics.

[31]  Giancarlo Fortino,et al.  A framework for collaborative computing and multi-sensor data fusion in body sensor networks , 2015, Inf. Fusion.

[32]  Peter Robinson,et al.  Dimensional affect recognition using Continuous Conditional Random Fields , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[33]  Min Chen,et al.  AIWAC: affective interaction through wearable computing and cloud technology , 2015, IEEE Wireless Communications.

[34]  Min Chen,et al.  A unified control and optimization framework for dynamical service chaining in software-defined NFV system , 2015, IEEE Wireless Communications.

[35]  Victor C. M. Leung,et al.  CAP: community activity prediction based on big data analysis , 2014, IEEE Network.

[36]  M. Shamim Hossain,et al.  Audio–Visual Emotion-Aware Cloud Gaming Framework , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[37]  Mohammad Soleymani,et al.  Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection , 2016, IEEE Transactions on Affective Computing.

[38]  Tarik Taleb,et al.  Toward carrier cloud: Potential, challenges, and solutions , 2014, IEEE Wireless Communications.

[39]  Xiaohu Ge,et al.  Analysis of the Uplink Maximum Achievable Rate With Location-Dependent Intercell Signal Interference Factors Based on Linear Wyner Model , 2013, IEEE Transactions on Vehicular Technology.