Kai Kunze
Paul Lukowicz
ABSTRACT
We present a set of heuristics that significantly increase the robustness of motion sensor-based activity recognition with respect to sensor displacement. In this paper placement refers to the position within a single body part (e.g, lower arm). We show how, within certain limits and with modest quality degradation, motion sensorbased activity recognition can be implemented in a displacement tolerant way. We first describe the physical principles that lead to our heuristic. We then evaluate them first on a set of synthetic lower arm motions which are well suited to illustrate the strengths and limits of our approach, then on an extended modes of locomotion problem (sensors on the upper leg) and finally on a set of exercises performed on various gym machines (sensors placed on the lower arm). In this example our heuristic raises the displaced recognition rate from 24% for a displaced accelerometer, which had 96% recognition when not displaced, to 82%.
- Aylward, R., Paradiso, J.: Sensemble: a wireless, compact, multi-user sensor system for interactive dance. Proceedings of the 2006 conference on New interfaces for musical expression (2006) 134--139 Google Scholar
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- Brashear, H., Starner, T., Lukowicz, P., Junker, H.: Using multiple sensors for mobile sign language recognition. Wearable Computers, 2003. Proceedings. Seventh IEEE International Symposium on (2003) 45--52 Google ScholarDigital Library
- Kern, N., Schiele, B., Junker, H., Lukowicz, P., Tröster, G.: Wearable sensing to annotate meeting recordings. Personal and Ubiquitous Computing 7 (2003) 263--274 Google ScholarDigital Library
- Kunze, K., Lukowicz, P., Junker, H., Troester, G.: Where am i: Recognizing on-body positions of wearable sensors. LOCA'04: International Workshop on Locationand Context- .. (2005) Google ScholarDigital Library
- Krause, A., Smailagic, A., Siewiorek, D.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable .. IEEE Transactions on Mobile Computing (2006) Google ScholarDigital Library
- Lester, J., Hannaford, B., Boriello, G.: Are you with me?--using accelerometers to determine if two devices are carried by the same person. Pervasive Computing: Second International Conference (2004)Google Scholar
- Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. Proceedings of Pervasive (2006) Google ScholarDigital Library
- Mantyjarvi, J., Himberg, J., Seppanen, T., Center, N. R.: Recognizing human motion with multiple acceleration sensors. Systems, Man, and Cybernetics, 2001 IEEE International Conference on 2 (2001)Google Scholar
- Maurer, U., Smailagic, A., Siewiorek, D., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. Proceedings of the International Workshop on Wearable and .. (2006) Google ScholarDigital Library
- Mizell, D.: Using gravity to estimate accelerometer orientation. Wearable Computers, 2003. Proceedings. Seventh IEEE International Symposium on (2005) 252--253 Google ScholarDigital Library
- Stiefmeier, T. Roggen, D. Troster, G. Ogris, G. Lukowicz, P.: Wearable activity tracking in car manufacturing. To. Appear, IEEE Pervasive Computing 7 (2008) Google ScholarDigital Library
- Randell, C., Muller, H.: Context awareness by analysing accelerometer data. The Fourth International Symposium on Wearable Computers 1 (2000) 175--176 Google ScholarDigital Library
- Roggen, D., Bharatula, N., Stager, M., Lukowicz, P., Troster, G.: From sensors to miniature networked sensorbuttons. Proceedings of the 3rd International Conference on Networked Sensing Systems (INSS06) (2006)Google Scholar
- Van Laerhoven, K., Aronsen, A.: Memorizing what you did last week: Towards detailed actigraphy with a wearable sensor. Proceedings of the 27th International Conference on.. (2007) Google ScholarDigital Library
- Van Laerhoven, K., Cakmakci, O.: What shall we teach our pants? Wearable Computers, 2000. The Fourth International Symposium on (2000) 77--83 Google ScholarDigital Library
- Van Laerhoven, K., Gellersen, H.: Spine versus porcupine: a study in distributed wearable activity recognition. (ISWC 2004) Google ScholarDigital Library
- Westeyn, T., Vadas, K., Bian, X., Starner, T., Abowd, G.: Recognizing mimicked autistic self-stimulatory behaviors using hmms. IEEE International Symposium on Wearable Computers (2005) 164--169 Google ScholarDigital Library
- Zinnen, A., van Laerhoven, K., Schiele, B.: Toward recognition of short and non-repetitive activities from wearable sensors. (European Conference on Ambient Intelligence 2007) Google ScholarDigital Library
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