Abstract
Activity recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware computing. Most approaches for activity recognition focus on repetitive or long time patterns within the data. There is however high interest in recognizing very short activities as well, such as pushing and pulling an oil stick or opening an oil container as sub-tasks of checking the oil level in a car. This paper presents a method for the latter type of activity recognition using start and end postures (short fixed positions of the wrist) in order to identify segments of interest in a continuous data stream. Experiments show high discriminative power for using postures to recognize short activities in continuous recordings. Additionally, classifications using postures and HMMs for recognition are combined.
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References
Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services, ACM Press, New York, USA (2005)
Lukowicz, P., Ward, J., Junker, H., Staeger, M., Troester, G., Atrash, A., Starner, S.: Recognizing workshop activity using body worn microphones and accelerometers. In: Pervasive Computing (2005)
Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Mattern, F. (ed.) Pervasive Computing (2004)
Minnen, D., Starner, T., Essa, I., Isbell, C.: Discovering Characteristic Actions from On-Body Sensor Data. In: Proceedings of the Tenth IEEE International Symposium on Wearable Computers (ISWC), IEEE Computer Society Press, Los Alamitos (2006)
Clarkson, B., Pentland, A.: Unsupervised clustering of ambulatory audio and video. In: Icassp (1999)
Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Proceedings of the Fourth IEEE Int. Conf. on Multimodal Interfaces, IEEE Computer Society Press, Los Alamitos (2002)
Kela, J., Korpipää, P., Mäntyjärvi, J., Kallio, S., Savino, G., Jozzo, L., Marca, D.: Accelerometer-based gesture control for a design environment. Personal Ubiquitous Comput. (2006)
Mäntylaä, Mäntyjärvi, Seppänen, Tuulari: Hand gesture recognition of a mobile device user. In: The Proceedings of the international IEEE conference on multimedia and expo (2000)
Hoffman, F., Heyer, P., Hommel, G.: Velocity profile based recognition of dynamic gestures with discrete hidden markov models. In: Proceedings of gesture workshop (1997)
Iacucci, G., Kela, J., Pehkonen, P.: Computational support to record and re-experience visits. Personal and ubiquitous computing 8(2) (2004)
Ward, J., Lukowicz, P., Tröster, G.: Gesture spotting using wrist worn microphone and 3-axis accelerometer. In: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies (2005)
Lukowicz, P., Ward, J., Junker, H., Stäger, M., Tröster, G., Atrash, A., Starner, T.: Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. LNCS (2004)
Murphy, K.: Hidden Markov Model (HMM) Toolbox for Matlab, http://www.cs.ubc.ca/murphyk/Software/HMM/hmm.html
Stiefmeier, T., Ogris, G., Junker, H., Lukowicz, P., Tröster, G.: Combining Motion Sensors and Ultrasonic Hands Tracking for Continuous Activity Recognition in a Maintenance Scenario. In: 10th IEEE International Symposium on Wearable Computers (2006)
Clarkson, B., Sawhney, N., Pentland, A.: Auditory context awareness in wearable computing. In: Workshop on Perceptual User Interfaces (1998)
Rabiner, L.: A Tutorial On Hidden Markov Models. Proceedings of the IEEE (1989)
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Zinnen, A., van Laerhoven, K., Schiele, B. (2007). Toward Recognition of Short and Non-repetitive Activities from Wearable Sensors. In: Schiele, B., et al. Ambient Intelligence. AmI 2007. Lecture Notes in Computer Science, vol 4794. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76652-0_9
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DOI: https://doi.org/10.1007/978-3-540-76652-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76651-3
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