Monitoring Dressing Activity Failures through RFID and Video

A. Matic; P. Mehta; J. M. Rehg; V. Osmani; O. Mayora

doi:10.3414/ME10-02-0026

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2012; 51(01): 45-54
DOI: 10.3414/ME10-02-0026

Original Articles

Schattauer GmbH

Monitoring Dressing Activity Failures through RFID and Video

Authors

A. Matic

¹Ubiquitous Interaction Group, CREATE-NET, Trento, Italy
P. Mehta

²Health Systems Institute and GVU Center, School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA
J. M. Rehg

²Health Systems Institute and GVU Center, School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA
V. Osmani

¹Ubiquitous Interaction Group, CREATE-NET, Trento, Italy
O. Mayora

¹Ubiquitous Interaction Group, CREATE-NET, Trento, Italy

Abstract

Summary

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.

Keywords

Pervasive healthcare - monitoring activities of daily living (ADL) - assessing dressing activity

Full Text

References

References
1 Stikic M, Huynh T, Laerhoven KV, Schiele B. ADL Recognition Based on the Combination of RFID and Accelerometer Sensing. PervasiveHealth 2008. Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare 2008
2 www.aoa.gov (Internet). Administration of Aging; (cited 2009 November 9)
3 Morris M, Lundell J, Dishman E, Needham B. New Perspectives on Ubiquitous Computing from Ethnographic Study of Elders with Cognitive Decline. UbiComp’03: Proceedings of the 5th International Conference on Ubiquitous Computing 2003
4 Philipose M, Fishkin K, Perkowitz M, Petterson D, Fox D, Kautz H. et al Inferring Activities from Interactions with Objects. Proceedings of the 2nd International Conference on Pervasive Computing 2004
5 Walker CM, Sunderland A, Sharma J, Walker MF. The impact of cognitive impairment on upper body dressing difficulties after stroke: a video analysis of patterns of recovery. J Neurol Neurosurg Psychiatry 2004; 75: 43-48.
6 Peters C, Wachsmuth S, Hoey J. Learning to recognise behaviours of persons with dementia using multiple cues in an HMM-based approach. Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments 2009
7 Mihailidis A, Carmichael B, Boger J. The Use of Computer Vision in an Intelligent Environment to Support Aging-in-Place, Safety, and Independence in the Home. IEEE Transactions on Informations Technology in Biomedicine 2004; 8 (003) 238-247.
8 Mynatt ED, Melenhorst AS, Fisk AD, Rogers WA. Aware Technologies for Aging in Place: Understanding User needs and Attitudes. Proceedings of the 2nd International Conference on Pervasive Computing 2004
9 Hayes TL, Hunt JM, Adami A, Kaye JA. An Electronic Pillbox for Continuous Monitoring of Medication Adherence. Proceedings of the 28th IEEE EMBS Annual International Conference; New York City, USA 2006
10 Kaye J. Home based technologies: A new paradigm for conducting dementia prevention trials. Alzheimer’s&Dementia, The journal of the Alzheimer’s Association. 2008; 4 (001) Suppl 1 S60-66.
11 Feyereisen P, Gendron M, Seron X. Disorders of Everyday Actions in Subjects Suffering from Senile Dementia of Alzheimer’s Type: An Analysis of Dressing Performance. Neuropsychological Rehabilitation 1999; 9 (002) 169-188.
12 Namazi K. Dressing independently: A closet modification model for Alzheimer’s disease patients. American Journal of Alzheimer’s Disease and Other Dementias 1992; 7 (001) 22-28.
13 Brown C, Moore WP, Hemman D, Yunek A. Influence if instrumental activities of daily living assessment method on judgements of independence. American Journal of Occupational Therapy 1996; 50 (003) 202-206.
14 Alzheimer’s Australia An Australian Government Initiative. Dressing - Caring for someone with dementia. 2005
15 Lyketsos CG, Rabins PV.. Dementia Care Guidelines for Families. Division of Geriatric Psychiatry and Neuropsychiatry The Johns Hopkins University 2006
16 Bãlan AO, Black MJ. The Naked Truth:Estimating Body Shape Under Clothing. Proceedings of the 10th European Conference on Computer Vision 2008
17 Wu J, Osuuntogun A, Choudhury T, Philipose M, Rehg JM. A Scalable Approach to Activity Recognition based on Object Use. ICCV 2007 : Proceedings of the 11th IEEE International Conference on Computer Vision 2007
18 Dalton AF, Morgan F, Laighin GO. A preliminary study of using wireless kinematic sensors to identify basic Activities of Daily Living. Proceedings of the 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada 2008
19 Fleury A, Noury N, Vacher M. Supervised Classification of Activituies of Daily Living in Health Smart Homes using SVM. Proceedings of the 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA 2009
20 Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection. Proceedings of the International Conference on Computer Vision & Pattern Recognition 2005; pp 886-893.
21 Lobay A, Forsyth DA. Recovering Shape and Irradiance Maps from rich dense texton fields. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004
22 Zhang W, Beggole J, Chu M, Liu JJ, Yee N. Real-time clothes comparison based on multi-view vision. Proceedings of the 2nd ACM/IEEE International Conference on Distributed Smart Cameras 2008
23 Gallagher A, Chen T. Clothing Cosegmentation for Recognizing People. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2008
24 Arnrich B, Mayora O, Bardram J, Troster G. Pervasive Healthcare: Paving the Way for a Pervasive, User-centered and Prevemntive Healtcare Model. Methods Inf Med 2010; 49 (001) 67-73.
25 Cook DJ, Schmitter-Edgecombe M. Assessing the Quality of Activities in a Smart Environment. Methods Inf Med 2009; 48 (005) 480-485.
26 Koch S, Marschollek M, Wolf KH, Plischke M, Haux R. On Health-enabling and Ambient-assistive Technologies. Methods Inf Med 2008; 28 (001) 29-37.