Understanding people with human activities and social interactions for human-centered computing

An ultimate goal of human-centered computing is making human beings the center of computing technologies. To make people a core component of the technology, first of all, we need to understand people and society in which people live. In this paper, we propose two important factors in order to understand people and their social interactions. Proposed human activity recognition and neighbor discovery schemes help us comprehend human activities and their social behaviors. In addition, combination of these two mechanisms provide us with an opportunity for better understanding of people in the near future. Finally, it might be worth analyzing correlation between human activities and social interactions for the group of people using our proposed schemes.

[1]  P H Veltink,et al.  Detection of static and dynamic activities using uniaxial accelerometers. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  Yu-Chee Tseng,et al.  Quorum-Based Asynchronous Power-Saving Protocols for IEEE 802.11 Ad Hoc Networks , 2005, Mob. Networks Appl..

[3]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  Rong Zheng,et al.  Asynchronous wakeup for ad hoc networks , 2003, MobiHoc '03.

[5]  Nicu Sebe,et al.  Guest Editors' Introduction: Human-Centered Computing--Toward a Human Revolution , 2007, Computer.

[6]  G M Lyons,et al.  Long-term mobility monitoring of older adults using accelerometers in a clinical environment , 2004, Clinical rehabilitation.

[7]  郑萌,et al.  Heterogeneous multi-channel neighbor discovery for mobile sensing applications: theoretical foundation and protocol design , 2014 .

[8]  Athanasios V. Vasilakos,et al.  Spatial Reusability-Aware Routing in Multi-Hop Wireless Networks , 2016, IEEE Transactions on Computers.

[9]  Kaigui Bian,et al.  Heterogeneous multi-channel neighbor discovery formobile sensing applications: theoretical foundationand protocol design , 2014, MobiHoc '14.

[10]  Charles J. Colbourn,et al.  Combinatorial design theory , 1987 .

[11]  Guihai Chen,et al.  On designing neighbor discovery protocols: A code-based approach , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[12]  Thomas S. Huang,et al.  Human-Centered Computing : Toward a Human Revolution , 2007 .

[13]  Steven A. Borbash,et al.  Birthday protocols for low energy deployment and flexible neighbor discovery in ad hoc wireless networks , 2001, MobiHoc '01.

[14]  G M Lyons,et al.  A description of an accelerometer-based mobility monitoring technique. , 2005, Medical engineering & physics.

[15]  Kamiar Aminian,et al.  Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly , 2003, IEEE Transactions on Biomedical Engineering.

[16]  Athanasios V. Vasilakos,et al.  Tight Performance Bounds of Multihop Fair Access for MAC Protocols in Wireless Sensor Networks and Underwater Sensor Networks , 2012, IEEE Transactions on Mobile Computing.

[17]  Douglas R. Stinson,et al.  Combinatorial designs: constructions and analysis , 2003, SIGA.

[18]  Jong-Hoon Youn,et al.  On-board processing of acceleration data for real-time activity classification , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[19]  C. Colbourn,et al.  The CRC handbook of combinatorial designs , edited by Charles J. Colbourn and Jeffrey H. Dinitz. Pp. 784. $89.95. 1996. ISBN 0-8493-8948-8 (CRC). , 1997, The Mathematical Gazette.

[20]  Karthik Lakshmanan,et al.  U-connect: a low-latency energy-efficient asynchronous neighbor discovery protocol , 2010, IPSN '10.

[21]  I. Anderson.  Combinatorial Designs and Tournaments , 1998 .

[22]  Athanasios V. Vasilakos,et al.  EDAL: An Energy-Efficient, Delay-Aware, and Lifetime-Balancing Data Collection Protocol for Heterogeneous Wireless Sensor Networks , 2015, IEEE/ACM Transactions on Networking.

[23]  Yue Zhang,et al.  Interference-Based Topology Control Algorithm for Delay-Constrained Mobile Ad Hoc Networks , 2015, IEEE Transactions on Mobile Computing.

[24]  Peter Dukes,et al.  Combinatorial Design Theory , 2009 .

[25]  Robin Kravets,et al.  Searchlight: won't you be my neighbor? , 2012, Mobicom '12.

[26]  David E. Culler,et al.  Practical asynchronous neighbor discovery and rendezvous for mobile sensing applications , 2008, SenSys '08.

[27]  Athanasios V. Vasilakos,et al.  Local Area Prediction-Based Mobile Target Tracking in Wireless Sensor Networks , 2015, IEEE Transactions on Computers.

[28]  Athanasios V. Vasilakos,et al.  Oblivious neighbor discovery for wireless devices with directional antennas , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[29]  Jong-Hoon Youn,et al.  Block Design-Based Asynchronous Neighbor Discovery Protocol for Wireless Sensor Networks , 2015, J. Sensors.

[30]  Nicu Sebe,et al.  Human-centered computing: a multimedia perspective , 2006, MM '06.

[31]  Yu-Chee Tseng,et al.  Power-saving protocols for IEEE 802.11-based multi-hop ad hoc networks , 2003, Comput. Networks.