Opportunistic strategies for lightweight signal processing for body sensor networks

We present a mobile platform for body sensor networking based on a smartphone for lightweight signal processing of sensor mote data. The platform allows for local processing of data at both the sensor mote and smartphone levels, reducing the overhead of data transmission to remote services. We discuss how the smartphone platform not only provides the ability for wearable signal processing, but it allows for opportunistic sensing strategies, in which many of the onboard sensors and capabilities of modern smartphones may be collected and fused with body sensor data to provide environmental and social context. We propose that this can help refine data reduction at the local level. We describe three examples related to health and wellness, to which our system has been applied.

[1]  Ruzena Bajcsy,et al.  WAVE and CalFit — Towards social interaction in mobile body sensor networks , 2010, 2010 The 5th Annual ICST Wireless Internet Conference (WICON).

[2]  Wayne H. Wolf.  Applications and Architectures , 2004, Computer.

[3]  Matt Welsh,et al.  Sensor networks for emergency response: challenges and opportunities , 2004, IEEE Pervasive Computing.

[4]  Allen Y. Yang,et al.  DexterNet: An Open Platform for Heterogeneous Body Sensor Networks and its Applications , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[5]  Aleksandar Milenkovic,et al.  Journal of Neuroengineering and Rehabilitation Open Access a Wireless Body Area Network of Intelligent Motion Sensors for Computer Assisted Physical Rehabilitation , 2005 .

[6]  Fabio Bellifemine,et al.  Development of Body Sensor Network applications using SPINE , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[7]  Takeo Kanade,et al.  Image matching in large scale indoor environment , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[8]  Alex Pentland,et al.  MIThril 2003: applications and architecture , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[9]  Deborah Estrin,et al.  Ambulation: A Tool for Monitoring Mobility Patterns over Time Using Mobile Phones , 2009, 2009 International Conference on Computational Science and Engineering.

[10]  Jonathan R. Agre,et al.  An Integrated Architecture for Cooperative Sensing Networks , 2000, Computer.

[11]  Minho Shin,et al.  Anonysense: privacy-aware people-centric sensing , 2008, MobiSys '08.

[12]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..

[13]  Joel A. Hesch,et al.  A 3D pose estimator for the visually impaired , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Rong Chen,et al.  Research on Mobile Location Service Design Based on Android , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[15]  Rimon Elias,et al.  Fast localization in indoor environments , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[16]  Allen Y. Yang,et al.  A wireless body sensor network for the prevention and management of asthma , 2009, 2009 IEEE International Symposium on Industrial Embedded Systems.

[17]  Stefan Edlich,et al.  A location-based notification- and visualization-system indicating social activities , 2009, Electronic Imaging.

[18]  A Min Tjoa,et al.  A Smart Indoor Navigation Solution Based on Building Information Model and Google Android , 2008, ICCHP.

[19]  Jari Hyttinen,et al.  Assessment of Breathing Parameters during Running with a Wearable Bioimpedance Device , 2009 .

[20]  W. Yu,et al.  Environmental-Adaptive RSSI-Based Indoor Localization , 2009, IEEE Transactions on Automation Science and Engineering.