Gerald Bieber
ABSTRACT
Sufficient physical activity is required for everybody, especially for elderly people. Monitoring of physical activity is possible in daily life by using mobile sensors such as acceleration sensors. The recognition of periodic activity types like walking, cycling, car driving etc. is easy to perform. However, the identification of transitions between physical activities is difficult, because those events are nonrecurring and unique. The estimation about the share of standing or sitting during work is interesting for the design of the modern workplace. Human ergonomics demand for a limitation of standing work; this may even be enforced by the legal protection of working mothers to improve the working condition. The recognition of standing and sitting is furthermore useful within the home living area design. Hereby a detection of staying, sitting and walking supports the assessment of the activities of daily life. This paper addresses the methodology of mobile physical activity recognition of transitions between sitting and standing by using only one three-dimensional acceleration sensor. The recognition is performed by using a synthetic kernel signal and a correlation of the measurement signal. For the evaluation, a detection application has been developed which uses the build-in sensors of a standard mobile phone. The evaluation included 12 subjects and the result shows that mobile recognition of activity transitions is possible.
- {WHO10} World Health Organization WHO, Recommended Amount of Physical Activity, factsheet, 2010Google Scholar
- Sauer P., Sauer T., Ergonomie am Arbeitsplatz, Kleiner Ergonomieführer, LINAK GmbH, Nidda, Germany, MA M9-03-116-A 06.03, 2003Google Scholar
- Lifson N., Gordon G. B., McClintock R., Measurement of total carbon dioxide production by means of D2O. J. Appl. Physiol. 7 (1955), pp. 704--710, 1955Google ScholarCross Ref
- DeVaul R. W., Dunn S., Real-Time Motion Classification for Wearable Computing Applications, Technical report, Media Laboratory, Massachusetts Institute of Technology, 2001Google Scholar
- Bao L. Intille S., Activity Recognition from User-Annotated Acceleration Data, Eds. A. Ferscha and F. Mattern (Eds.): Pervasive 2004, LNCS 3001, pp. 1--17, Springer-Verlag, 2004Google Scholar
- Lester J., Choudhury T., Borriello G., Consolvo S., Landay J., Everitt K., Smith I., Sensing and Modelling Activities to Support Physical Fitness, Ubicomp Workshop: Monitoring, Measuring and Motivating Exercise: Ubiquitous Computing to Support Fitness, October 2005Google Scholar
- Choudhury T., Consolvo S., Harrison B., Hightower J., LaMarca A., LeGrand L., Rahimi A., Rea A., Borriello G., Hemmingway B., Klasnja P., Koscher K., Landay J., Wyatt D., Haehnel D., The Mobile Sensing Platform: An Embedded Activity Recognition System, Pervasive computing april-june 2008, IEEE CS, pp. 33--41, 2008 Google ScholarDigital Library
- Maurer U. et al., Location and Activity Recognition Using eWatch: A Wearable Sensor Platform, Ambient Intelligence in Every Day Life, LNCS 3864, Springer, pp. 86--100, 2006 Google ScholarDigital Library
- Bieber G., Voskamp J., Urban B., Activity Recognition for everyday life on Mobile Phones, HCII 2009, San Francisco, USA, 2009 Google ScholarDigital Library
- Biermann H., Weißmantel H., Benutzerfreundliches und Seniorengerechtes Design (SENSI Regelkatalog), VDE-Fortschrittsberichte, Reihe 1 Konstruktionstechnik Nr.247, 2003, ISBN -318-324701-1Google Scholar
- Najafi B., Aminian K., Paraschiv-Ionescu A., Loew F., Buela C., Robert P., Ambulatory System for Human Motion Analysis Using a Kinematic Sensor: Monitoring of Daily Physical Activity in the Elderly, IEEE Trans Biomed Eng. 2003 Jun; 50(6):711--23. 2003Google ScholarCross Ref
- Foerster F., Smeja M., Fahrenberg J.; Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring, Forschungsgruppe Psychophysiologie, Universität Freiburg, Germany, 1999Google Scholar
Index Terms
- Mobile physical activity recognition of stand-up and sit-down transitions for user behavior analysis
Recommendations
Using physical activity for user behavior analysis
PETRA '08: Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive EnvironmentsPhysical activity is one important aspect in user behavior analysis. Abnormal movement behavior might be an indicator for an inappropriate lifestyle, insufficient social inclusion, or generally disadvantageous life conditions which might call for ...
Activity monitoring by fusion of optical and mechanical tracking technologies for user behavior analysis
PETRA '09: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive EnvironmentsPatient monitoring by camera surveillance within the private surrounding suffers on privacy issues as well as high requirements on sensor technology and infrastructure. Because of those limitations there is not much knowledge on user activities and ...
The hearing trousers pocket: activity recognition by alternative sensors
PETRA '11: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive EnvironmentsIn daily life, mobile phones accompany the user permanently and are worn often in the front pocket of the trousers. The sensors included in today's mobile phones can hence be used for ubiquitous assistance. For instance, the acceleration sensor could be ...
Comments