PHAR: Practical Human Activity Recognition

April 30th, 2010  |  Published in Research

The goal of this project is to create practical approaches to human activity recognition (HAR). HAR is the process of allowing IT devices, processes and infrastructures to be aware of the situations and activities of the human beings interacting with these systems. Awareness is typically enabled by applying data processing and machine learning techniques to the output of mobile device sensors being carried by the subject (i.e. a smartphone). This awareness allows systems to better understand the humans interacting with them, thereby improving the response of the system to user input. Ultimately the goal is to have the systems understand the human controllers and their situations so well that explicit input becomes unnecessary.

For practical HAR (PHAR) to become a reality, there are several challenges which need to be addressed. TecO addresses 3 main challenges in this project. The first challenge is to reduce power consumption of the activity recognition process. Battery capacity is quickly becoming the limiting factor for mobile devices as it does not scale with Moore’s Law. In order for such an approach to be practical, it cannot detract from the overall user experience by reducing the battery life of the device running the awareness algorithms. This challenge is specifically applicable to mobile smartphones, although it can be extended all types of mobile devices as well as city-scale applications where power consumption has an impact.

A further challenge is to be able to recognize the activities and situations of groups of users, rather than just individuals. Increasingly instrumented environments, such as meeting rooms, are dealing with multiple users at once. The context of the group can be of vital importance to understanding their requirements on the smart environment, and may also differ greatly from the activities of the individuals in the group. Therefore, activity-aware systems must also take this into consideration and observe groups as well as individuals. There difficulties come with the scale of individuals and devices as groups approach the size of crowds, as well as the complex nature of the interactions between group individuals.

Another challenge addressed by PHAR is that of allowing recognition algorithms to adapt to the humans using them. As time goes on, the behavior of human beings changes, where activities are performed differently, some activities are no longer performed, and some activities. Therefore, the awareness of the devices must be fitted not only to the individual, but must be optimized periodically to make sure they change with the user. PHAR researches using a server-based architecture for activity recognition as a service. A service-based architecture allows individuals to benifit from the progress of the community as a whole by allowing local improvements to be extended to the community. Through modularization, it also allows local intelligence to by extend or cropped to account for appearing and disappearing activities, and for adaptation to changes in existing activities.

The project is funded by, and in collaboration with, SAP Reasearch.

Selected Publications

Stephan Sigg, Markus Scholz, Shuyu Shi, Yusheng Ji, Michael Beigl (2013) RF-Sensing of Activities From Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals, IEEE Transactions on Mobile Computing 99(1), p. 1-1, IEEE, url

Dawud Gordon, Jürgen Czerny, Michael Beigl (2013) Activity recognition for creatures of habit, Personal and Ubiquitous Computing, url, doi:10.1007/s00779-013-0638-2

Stephan Sigg, Dawud Gordon, Georg Von Zengen, Michael Beigl, Sandra Haseloff, Klaus David (2012) Investigation of Context Prediction Accuracy for Different Context Abstraction Levels, IEEE Transactions on Mobile Computing 11(6), p. 1047-1059, pdf

Dawud Gordon, Jurgen Czerny, Takashi Miyaki, Michael Beigl (2012) Energy-Efficient Activity Recognition Using Prediction, 16th International Symposium on Wearable Computers, p. 29-36, IEEE, Best Paper Nominee, Honorable Mention Award, pdf, doi:10.1109/ISWC.2012.25

Markus Scholz, Stephan Sigg, Gerrit Bagschik, Toni Guenther, Georg Von, Dimana Shiskova, Yusheng Ji, Michael Beigl (2011) SenseWaves : Radiowaves for context recognition, p. 1-4

Yong Ding, Matthias Budde, Dawud Gordon, Dimana Shishkova, Nadezda Sackmann, Hedda R Schmidtke, Michael Beigl (2011) Ubiquitous Know-How Transfer Based on a Mobile Learning and Classification System, Seventh International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT'11) Adjunct Proceedings, p. 3-4, pdf

Markus Scholz, Stephan Sigg, Hedda R Schmidtke, Michael Beigl (2011) Challenges for device-free radio-based activity recognition, 3rd Workshop on Context-Systems Design, Evaluation and Optimisation (CoSDEO 2011) in conjunction with MobiQuitous 2011, Kopenhagen, Denmark, p. 1-12, pdf

Dawud Gordon, Jan-Hendrik Hanne, Martin Berchtold, Takashi Miyaki, Michael Beigl (2011) Recognizing Group Activities using Wearable Sensors, 8th International ICST Conference on Mobile and Ubiquitous Systems (MobiQuitous 2011), p. 350 - 361, Copenhagen, Denmark: Springer Berlin Heidelberg, url

Dawud Gordon, Stephan Sigg, Yong Ding, Michael Beigl (2011) Using prediction to conserve energy in recognition on mobile devices, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), p. 364-367, IEEE, url, doi:http://dx.doi.org/10.1109/PERCOMW.2011.5766907

Dawud Gordon, Markus Scholz, Yong Ding, Michael Beigl (2011) Global Peer-to-Peer Classification in Mobile Ad-Hoc Networks: A Requirements Analysis, The 7th International and Interdisciplinary Conference on Modeling and Using Context 6967, Michael Beigl, Henning Christiansen, Thomas R. Roth-Berghofer, Anders Kofod-Petersen, Kenny R. Coventry, Hedda R. Schmidtke (ed.), p. 108-114, Karlsruhe, Germany: Springer Berlin Heidelberg, url, doi:10.1007/978-3-642-24279-3

Dawud Gordon, Jan-Hendrik Hanne, Martin Berchtold, Takashi Miyaki, Michael Beigl (2011) An Experiment in Hierarchical Recognition of Group Activities Using Wearable Sensors, The 7th International and Interdisciplinary Conference on Modeling and Using Context 6967, Michael Beigl, Henning Christiansen, Thomas R. Roth-Berghofer, Anders Kofod-Petersen, Kenny R. Coventry, Hedda R. Schmidtke (ed.), p. 104-107, Karlsruhe, Germany: Springer Berlin Heidelberg, url, doi:10.1007/978-3-642-24279-3

Matthias Budde, Martin Berchtold, Michael Beigl (2011) Activity Recognition on Mobile Phones - Why do we need it and how can it be done?, The 9th International Conference on Pervasive Computing (Pervasive 2011), Best Video Nominee, watch on youtube [expand]

Dawud Gordon, H Schmidtke, Michael Beigl (2010) Introducing new sensors for activity recognition, How To Do Good Research In Activity Recognition: Experimental methodology, performance evaluation and reproducibility. Workshop in conjunction with Pervasive 2010, pdf

Henning Günther, Firas El Simrany, Martin Berchtold, Michael Beigl (2010) A Tool Chain for a Lightweight , Robust and Uncertainty-based Context Classification System ( CCS ), 23rd International Conference on Architecture of Computing Systems (ARCS), 1st Workshop on Context-Systems Design, Evaluation and Optimization (CoSDEO), pdf

Martin Berchtold, G Henning, Michael Beigl (2010) A Demonstration of a Robust Context Classification System ( CCS ) and its Context ToolChain ( CTC ), Demonstration on the Eighth International Conference on Pervasive Computing (Pervasive'10), pdf

Dawud Gordon, Hedda Rahel Schmidtke, Michael Beigl, Georg von Zengen (2010) A novel micro-vibration sensor for activity recognition: Potential and limitations, International Symposium on Wearable Computers (ISWC) 2010, p. 1-8, IEEE, Best Paper Nominee, url, doi:10.1109/ISWC.2010.5665861

Martin Berchtold, Matthias Budde, Hedda R. Schmidtke, Michael Beigl (2010) An Extensible Modular Recognition Concept that Makes Activity Recognition Practical, 33rd Annual German Conference on Advances in Artificial Intelligence (KI 2010, p. 400-409, Springer Berlin / Heidelberg

Martin Berchtold, Matthias Budde, Dawud Gordon, Hedda R. Schmidtke, Michael Beigl (2010) ActiServ: Activity Recognition Service for mobile phones, International Symposium on Wearable Computers (ISWC) 2010 i, p. 1-8, IEEE, Best Paper Nominee, url, doi:10.1109/ISWC.2010.5665868

Yong Ding, Hedda R Schmidtke, Michael Beigl (2010) Beyond context-awareness: context prediction in an industrial application, Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing, p. 401-402, New York, NY, USA: ACM, url, doi:http://doi.acm.org/10.1145/1864431.1864457

Martin Berchtold, Till Riedel, Christian Decker, Kristof Van Laerhoven (2008) Gath-Geva specification and genetic generalization of Takagi-Sugeno-Kang fuzzy models, 2008 IEEE International Conference on Systems Man and Cybernetics, p. 595-600, Ieee, url, doi:10.1109/ICSMC.2008.4811342

Stephan Sigg, Tino Loeffler, Sandra Haseloff, Klaus David (2006) A Flexible Architecture For Context Aware Applications, Workshop: Selbstorganisierende, adaptive und kontextsensitive Systeme (SAKS)

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