Posture recognition for the elderly based on wireless sensor networks

Wireless-sensor-network-based home monitoring for elderly activity behavior involves functional assessment of daily activities. In this paper, a mechanism for estimation of elderly postures through various sensors is presented. The data from the triaxial acceleration sensor placed on the waist and the pressure sensors placed under the insoles are consolidated by the coordinator to distinguish the posture of the elderly, such as standing, sitting, walking, and falling down. The developed system for monitoring and discrimination of the elderly postures is tested through the obtained data in the laboratory and the results are encouraging in distinguishing postures of the elderly.

[1]  Wendong Xiao,et al.  Real-time Physical Activity classification and tracking using wearble sensors , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[2]  Zhihai He,et al.  A real-time system for in-home activity monitoring of elders , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  M. Anwar Hossain,et al.  Virtual Caregiver: An Ambient-Aware Elderly Monitoring System , 2012, IEEE Transactions on Information Technology in Biomedicine.

[4]  Anthony Almudevar,et al.  Home monitoring using wearable radio frequency transmitters , 2008, Artif. Intell. Medicine.

[5]  Ig-Jae Kim,et al.  Activity Recognition Using Wearable Sensors for Elder Care , 2008, 2008 Second International Conference on Future Generation Communication and Networking.

[6]  Bing Zhang Health care applications based on ZigBee standard , 2010, 2010 International Conference On Computer Design and Applications.

[7]  Baharak Shakeri Aski,et al.  Intelligent video surveillance for monitoring fall detection of elderly in home environments , 2008, 2008 11th International Conference on Computer and Information Technology.

[8]  Y. Nishida,et al.  Sensor network for supporting elderly care home , 2004, Proceedings of IEEE Sensors, 2004..

[9]  Boreom Lee,et al.  Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description , 2011, IEEE Transactions on Information Technology in Biomedicine.

[10]  A. Bonfiglio,et al.  Arrays of pressure sensors based on organic field effect: A new perspective for non invasive monitoring , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  S. C. Mukhopadhyay,et al.  Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly , 2012, IEEE Sensors Journal.

[12]  R. K. Rayudu,et al.  Sensor data fusion to determine wellness of an elderly in intelligent home monitoring environment , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[13]  Neil Johnson,et al.  A smart sensor to detect the falls of the elderly , 2004, IEEE Pervasive Computing.

[14]  John A. Stankovic,et al.  Context-aware wireless sensor networks for assisted living and residential monitoring , 2008, IEEE Network.