Kazuya Murao
Yu Enokibori
Paula Lago
ABSTRACT
The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition.
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Cross Ref
- K. Murao, Y. Enokibori H. Gjoreski, P. Lago, T. Okita, P. Siirtola, K. Hiroi, P.M. Scholl, M. Ciliberto, K. Urano. 2021. 9th International Workshop on Human Activity Sensing Corpus and Applications (HASCA). Association for Computing Machinery, New York, NY, USA, 281–284. https://doi.org/10.1145/3460418.3479266Google ScholarDigital Library
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- Daniel Roggen, Alberto Calatroni, Mirco Rossi, Thomas Holleczek, Kilian Förster, Gerhard Tröster, Paul Lukowicz, David Bannach, Gerald Pirkl, Alois Ferscha, Jakob Doppler, Clemens Holzmann, Marc Kurz, Gerald Holl, Ricardo Chavarriaga, Hesam Sagha, Hamidreza Bayati, Marco Creatura, and José del R. Millàn. 2010. Collecting complex activity datasets in highly rich networked sensor environments. In 2010 Seventh International Conference on Networked Sensing Systems (INSS). 233–240. https://doi.org/10.1109/INSS.2010.5573462Google ScholarCross Ref
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