Extracting Intra- and Inter-activity Association Patterns from Daily Routines of Elders

One of the most challenging issues faced by many elders is the over-decreasing independence mainly caused by impaired physical, cognitive, and/or sensory abilities. Activity recognition can be used to help elders live longer in their own homes independently, by providing assurance of safety, instructing performance of activity and assessing cognitive status. In this work, we propose to discover both intra- and inter-activity association patterns from daily routines of elderly people. Specifically, a data mining method is proposed to extract the most frequent sequential sequences of steps inside each individual activity (i.e., intra-activity pattern) and activities (i.e., inter-activity pattern) of a set of daily activities. These patterns can then be used to model human daily activities for activity recognition purpose, or to directly instruct/prompt elders with impaired memory when they perform daily routines. The experimental results conducted on two individuals’ datasets of daily activities show that our proposed approach is workable to discover these association patterns.

[1]  Shengrui Wang,et al.  A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments , 2011, 2011 IEEE International Conference on Advanced Information Networking and Applications.

[2]  Bernt Schiele,et al.  Discovery of activity patterns using topic models , 2008 .

[3]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[4]  Shengrui Wang,et al.  ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation , 2012, Pervasive Mob. Comput..

[5]  Bernt Schiele,et al.  Location- and Context-Awareness, Third International Symposium, LoCA 2007, Oberpfaffenhofen, Germany, September 20-21, 2007, Proceedings , 2007, LoCA.

[6]  D.P. Siewiorek,et al.  Wearable computers , 1994, IEEE Potentials.

[7]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[8]  Martha E. Pollack,et al.  Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment , 2005, AI Mag..

[9]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[10]  Henry A. Kautz,et al.  Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.

[11]  Lawrence B. Holder,et al.  Discovering Activities to Recognize and Track in a Smart Environment , 2011, IEEE Transactions on Knowledge and Data Engineering.

[12]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[13]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[14]  Jian Lu,et al.  A Pattern Mining Approach to Sensor-Based Human Activity Recognition , 2011, IEEE Transactions on Knowledge and Data Engineering.

[15]  Bernt Schiele,et al.  Scalable Recognition of Daily Activities with Wearable Sensors , 2007, LoCA.

[16]  Andreas Savvides,et al.  Extracting spatiotemporal human activity patterns in assisted living using a home sensor network , 2008, PETRA '08.