Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly

The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional random field) to evaluate various sliding window based techniques for the real time prediction of activities in older subjects wearing a passive (batteryless) sensor enabled RFID tag. The system achieved maximum overall real time activity prediction accuracy of \(95\,\%\) using a time weighted windowing technique to aggregate contextual information to input sensor data.

[1]  S. Robinovitch,et al.  Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study , 2013, The Lancet.

[2]  D. Oliver,et al.  Prevention of falls in hospital inpatients: agendas for research and practice. , 2004, Age and ageing.

[3]  Albrecht Schmidt,et al.  Recognizing context for annotating a live life recording , 2007, Personal and Ubiquitous Computing.

[4]  D C Ranasinghe,et al.  Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions. , 2014, Gait & posture.

[5]  Sung-Bae Cho,et al.  Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.

[6]  Qinfeng Shi,et al.  Sensor enabled wearable RFID technology for mitigating the risk of falls near beds , 2013, 2013 IEEE International Conference on RFID (RFID).

[7]  Minos N. Garofalakis,et al.  An adaptive RFID middleware for supporting metaphysical data independence , 2008, The VLDB Journal.

[8]  Norbert Gyorbíró,et al.  An Activity Recognition System For Mobile Phones , 2009, Mob. Networks Appl..

[9]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[10]  A Nair,et al.  Prevention of Falls in Hospital Inpatients , 2005 .

[11]  Tae-Seong Kim,et al.  A single tri-axial accelerometer-based real-time personal life log system capable of activity classification and exercise information generation , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[12]  Jian Lu,et al.  A hierarchical approach to real-time activity recognition in body sensor networks , 2012, Pervasive Mob. Comput..

[13]  Clemens Becker,et al.  Fall prevention in nursing homes. , 2010, Clinics in geriatric medicine.

[14]  Li-Chen Fu,et al.  Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home , 2009, IEEE Transactions on Automation Science and Engineering.

[15]  Christopher Joseph Pal,et al.  Activity recognition using the velocity histories of tracked keypoints , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..

[17]  Jake K. Aggarwal,et al.  Human detection using depth information by Kinect , 2011, CVPR 2011 WORKSHOPS.

[18]  Damith C. Ranasinghe,et al.  Wearable Quarter-Wave Folded Microstrip Antenna for Passive UHF RFID Applications , 2013 .

[19]  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.