A wearable wireless sensor node for body fall detection

Wireless sensor networks (WSNs) are commonly considered as a key enabling technology for ambient assisted living (AAL) and smart home services. Essential requirements for this kind of applications are: small node size, long lifetime, little invasiveness and reliable communication over a range of few tens of meters even in presence of obstacles. For this reason, in this paper a low-cost WSN node architecture for body fall detection is presented. Our node is partially inspired to the well-known TelosB/Tmote Sky so as to maintain full software compatibility with TinyOS, but it is highly optimized in size, it is equipped with a chip antenna with better performance than the typical inverted-F printed one and it is powered by a compact high-capacity lithium-ion polymer rechargeable battery. Several experimental results confirm that the node lifetime may exceed one week of operation in the expected working conditions.

[1]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[2]  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.

[3]  Richard P. Martin,et al.  The limits of localization using signal strength: a comparative study , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[4]  Carlos Pomalaza-Raez,et al.  Monitoring Human Movements at Home Using Wearable Wireless Sensors , 2009 .

[5]  Piero Malcovati,et al.  Wearable wireless accelerometer with embedded fall-detection logic for multi-sensor ambient assisted living applications , 2009, 2009 IEEE Sensors.

[6]  David E. Culler,et al.  A practical evaluation of radio signal strength for ranging-based localization , 2007, MOCO.

[7]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[8]  George Papadopoulos,et al.  Battery Lifetime Prediction Model for a WSN Platform , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[9]  K.-P. Hoffmann,et al.  Autonomy Suitability of Wireless Modules for Ambient Assisted Living Applications: WiFi, Zigbee, and Proprietary Devices , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[10]  Luca Benini,et al.  Design of a Solar-Harvesting Circuit for Batteryless Embedded Systems , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  S. Venkatesan,et al.  Accelerometer-based human abnormal movement detection in wireless sensor networks , 2007, HealthNet '07.

[12]  S. Standard GUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT , 2006 .

[13]  Javier Reina-Tosina,et al.  Ambient Assisted Living: A methodological approach , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[14]  Peng Yu,et al.  The design of low-power wireless sensor node , 2010, 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings.

[15]  H. Martin,et al.  Analysis of key aspects to manage wireless sensor networks in ambient assisted living environments , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.

[16]  Manel Gasulla,et al.  Runtime Extension of Low-Power Wireless Sensor Nodes Using Hybrid-Storage Units , 2010, IEEE Transactions on Instrumentation and Measurement.

[17]  Dario Petri,et al.  Accurate Software-Related Average Current Drain Measurements in Embedded Systems , 2007, IEEE Transactions on Instrumentation and Measurement.

[18]  J. Vanfleteren,et al.  Design of flexible, low-power and wireless sensor nodes for human posture tracking aiding epileptic seizure detection , 2009, 2009 IEEE Sensors.

[19]  Dario Petri,et al.  Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment , 2011, IEEE Transactions on Instrumentation and Measurement.

[20]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.