Monitoring Dressing Activity Failures through RFID and Video

BACKGROUND Monitoring and evaluation of Activities of Daily Living in general, and dressing activity in particular, is an important indicator in the evaluation of the overall cognitive state of patients. In addition, the effectiveness of therapy in patients with motor impairments caused by a stroke, for example, can be measured through long-term monitoring of dressing activity. However, automatic monitoring of dressing activity has not received significant attention in the current literature. OBJECTIVES Considering the importance of monitoring dressing activity, the main goal of this work was to investigate the possibility of recognizing dressing activities and automatically identifying common failures exhibited by patients suffering from motor or cognitive impairments. METHODS The system developed for this purpose comprised analysis of RFID (radio frequency identification) tracking and computer vision processing. Eleven test subjects, not connected to the research, were recruited and asked to perform the dressing task by choosing any combination of clothes without further assistance. Initially the test subjects performed correct dressing and then they were free to choose from a set of dressing failures identified from the current research literature. RESULTS The developed system was capable of automatically recognizing common dressing failures. In total, there were four dressing failures observed for upper garments and three failures for lower garments, in addition to recognizing successful dressing. The recognition rate for identified dressing failures was between 80% and 100%. CONCLUSIONS We developed a robust system to monitor the dressing activity. Given the importance of monitoring the dressing activity as an indicator of both cognitive and motor skills the system allows for the possibility of long term tracking and continuous evaluation of the dressing task. Long term monitoring can be used in rehabilitation and cognitive skills evaluation.

[1]  G Tröster,et al.  Pervasive Healthcare , 2009, Methods of Information in Medicine.

[2]  Andre Gustavo Adami,et al.  An Electronic Pillbox for Continuous Monitoring of Medication Adherence , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Alex Mihailidis,et al.  The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home , 2004, IEEE Transactions on Information Technology in Biomedicine.

[4]  J. Kaye Home-based technologies: A new paradigm for conducting dementia prevention trials , 2008, Alzheimer's & Dementia.

[5]  Michael J. Black,et al.  The Naked Truth: Estimating Body Shape Under Clothing , 2008, ECCV.

[6]  Pierre Feyereisen,et al.  Disorders of Everyday Actions in Subjects Suffering from Senile Dementia of Alzheimer's Type: An Analysis of Dressing Performance , 1999 .

[7]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Diane J Cook,et al.  Assessing the Quality of Activities in a Smart Environment , 2009, Methods of Information in Medicine.

[9]  S. Koch,et al.  On Health-enabling and Ambient-assistive Technologies , 2009, Methods of Information in Medicine.

[10]  N. Noury,et al.  Supervised classification of activities of daily living in health smart homes using SVM , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Jay Lundell,et al.  New Perspectives on Ubiquitous Computing from Ethnographic Study of Elders with Cognitive Decline , 2003, UbiComp.

[12]  Jesse Hoey,et al.  Learning to recognise behaviours of persons with dementia using multiple cues in an HMM-based approach , 2009, PETRA '09.

[13]  K. H. Namazi,et al.  Dressing independently: A closet modification model for Alzheimer's disease patients , 1992 .

[14]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[15]  James Begole,et al.  Real-time clothes comparison based on multi-view vision , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[16]  Fearghal Morgan,et al.  A preliminary study of using wireless kinematic sensors to identify basic Activities of Daily Living , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Arthur D. Fisk,et al.  Aware technologies for aging in place: understanding user needs and attitudes , 2004, IEEE Pervasive Computing.

[18]  C. Dougan,et al.  A NEUROLOGIST'S APPROACH TO THE IMMUNOSUPPRESSED PATIENT , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

[19]  Bernt Schiele,et al.  ADL recognition based on the combination of RFID and accelerometer sensing , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[20]  Tsuhan Chen,et al.  Clothing cosegmentation for recognizing people , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  James M. Rehg,et al.  A Scalable Approach to Activity Recognition based on Object Use , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  M. Walker,et al.  The impact of cognitive impairment on upper body dressing difficulties after stroke: a video analysis of patterns of recovery. , 2004, Journal of neurology, neurosurgery, and psychiatry.

[23]  D. Forsyth,et al.  Recovering shape and irradiance maps from rich dense texton fields , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[24]  W P Moore,et al.  Influence of instrumental activities of daily living assessment method on judgments of independence. , 1996, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[25]  O. Mayora,et al.  Pervasive or Ubiquitous Healthcare? , 2010, Methods of Information in Medicine.