Daily activity recognition system for the elderly using pressure sensors

As transformed to aging society rapidly, the number of old persons who live alone is drastically increased. Because these old people may have disorder of bodily function, and be suffering from geriatric disease, needs of a health assistance system to make them healthier are strongly increased. In this paper, we propose a daily activity recognition system for an old person using pressure sensors. The target daily activities are MEAL, SLEEP, EXCRETION, GO-OUT, and REST. The proposed system installs pressure sensors to furniture and floors in home, and recognizes daily activities based on the object usage information. By using the proposed system, we can provide a warning sign for unhealthy cases such as skipping meals. And, the families who live in remote place can check that their parent takes a healthy daily living.

[1]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[2]  François Brémond,et al.  Video understanding for complex activity recognition , 2006, Machine Vision and Applications.

[3]  M. Mathie,et al.  A pilot study of long-term monitoring of human movements in the home using accelerometry , 2004, Journal of telemedicine and telecare.

[4]  Matthai Philipose,et al.  Unsupervised Activity Recognition Using Automatically Mined Common Sense , 2005, AAAI.

[5]  Henry A. Kautz,et al.  Sensor-Based Understanding of Daily Life via Large-Scale Use of Common Sense , 2006, AAAI.

[6]  N. Papanikolopoulos,et al.  Vision-Based Human Tracking and Activity Recognition , 2003 .

[7]  Eliathamby Ambikairajah,et al.  Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. , 2006, Physiological measurement.

[8]  Monique Thonnat,et al.  Activity Recognition from Video Sequences using Declarative Models , 2000, ECAI.

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

[10]  Matthai Philipose,et al.  The Probabilistic Activity Toolkit: Towards Enabling Activity-Aware Computer Interfaces , 2003 .

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

[12]  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.

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