Mathias Ciliberto
ABSTRACT
Activity and gesture recognition from wearable sensors data can be used for skill assessment in order to gauge the capability of a user at performing a task. As many other problem of automatic classification, gesture recognition relies on annotated data for the training of the classification system and to gather a set of gestures for the assessment. The collection of a multi-sensors dataset for this goal can be challenging, especially when it is performed in the field rather than in a more controlled environment such as a laboratory. In this paper, we present the collection of a beach volleyball gestures dataset in the field. The resulting dataset is made publicly available to the community and it includes 585 annotated gestures, collected by 10 users, with 4 wearable inertial sensors per user. In addition, we also provide a list of lessons learnt, suggestions and guidelines to improve future data collections in the field.
- Mathias Ciliberto, Luis Ponce Cuspinera, and Daniel Roggen. 2018. Complex human gestures encoding from wearable inertial sensors for activity recognition. In Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks. ACM.Google Scholar
Digital Library
- Mathias Ciliberto, Lin Wang, Daniel Roggen, and Ruediger Zillmer. 2019. Drinking Gesture Recognition from Poorly Annotated Data: A Case Study. In Human Activity Sensing. Springer International Publishing, 71–89.Google Scholar
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- Daniel Roggen, Arash Pouryazdan, and Mathias Ciliberto. 2017. BlueSense: designing an extensible platform for wearable motion sensing, sensor research and IoT applications. In Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks. ACM.Google Scholar
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