Recognition of nursing activity with accelerometers and RFID

Purpose – This paper aims to construct a recognition system of nursing activities. Design/methodology/approach – The authors used accelerometers and radio frequency identification (RFID) tags to ensure patient privacy in practical nursing care environments. The accelerometers were attached to the body of the nurse, and the RFID was attached to apparatuses and objects. In addition, a pattern classification algorithm using a support vector machine and filtering methodology were applied. Findings – The accuracy using accelerometers and RFID was 73 percent. When the filtering algorithm was applied, the results were 79 percent. The results showed that activities with short execution times or those that resembled others in posture had low recognition accuracy. Research limitations/implications – Activities requiring only a short period of time tend to be misrecognized. Practical implications – It is possible to construct a training system for nursing activities with the system that recognizes the sequence of nu...

[1]  K. Fukunaga,et al.  Learning and recognizing behavioral patterns using position and posture of human , 2004 .

[2]  Futoshi Naya,et al.  Activity recognition from interactions with objects using dynamic Bayesian network , 2009, CASEMANS@Pervasive.

[3]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  Noriaki Kuwahara,et al.  Practical Design of A Sensor Network for Understanding Nursing Activities , 2006, Proceedings. 2006 31st IEEE Conference on Local Computer Networks.

[6]  Takeo Kanade,et al.  Intelligent Autonomous Systems , 1991, Robotics Auton. Syst..

[7]  Futoshi Naya,et al.  B-Pack: a Bluetooth-based wearable sensing device for nursing activity recognition , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[8]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[9]  Futoshi Naya,et al.  Workers' Routine Activity Recognition using Body Movements and Location Information , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[10]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

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

[12]  Roy Want,et al.  An introduction to RFID technology , 2006, IEEE Pervasive Computing.

[13]  Hiroshi Iseki,et al.  Wearable Auto-Event-Recording of Medical Nursing , 2003, INTERACT.

[14]  Kathryn A. Dowsland,et al.  Nurse scheduling with tabu search and strategic oscillation , 1998, Eur. J. Oper. Res..

[15]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Edmund K. Burke,et al.  A shift sequence based approach for nurse scheduling and a new benchmark dataset , 2010, J. Heuristics.

[17]  Peter H. Tu,et al.  Activity Recognition using Visual Tracking and RFID , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[18]  L. Eisen,et al.  Improving nurse-physician communication and satisfaction in the intensive care unit with a daily goals worksheet. , 2004, American journal of critical care : an official publication, American Association of Critical-Care Nurses.

[19]  Jun Ota,et al.  Dynamic scheduling-based inpatient nursing support: applicability evaluation by laboratory experiments , 2012, Int. J. Auton. Adapt. Commun. Syst..

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

[21]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .