A Multi-Sensor Setting Activity Recognition Simulation Tool
Open accessAbstract
Motion capture generates data which are often more accurate than those captured by multiple of accelerometer sensors by their physical specification. Based on the observation that accelerometer data can be obtained by the second derivation of position data from motion capture, we propose a simulator, called MEASURed, for activity recognition classifiers. MEASURed can accommodate any number of virtual accelerometer sensors on the body based on some given motion capture data. Therefore, MEASURed can evaluate activity recognition classifiers in settings with different number, placement, and sampling rate of accelerometer sensors. Our results show that the F1-Score estimated by MEASURed is close to that obtained with the real accelerometer data.
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Recent approaches addressing the lack of annotated datasets extract virtual movement data from modalities such as mocap and videos, which contain a large number of participants and a diverse set of actions and environments.
“Recent approaches addressing the lack of annotated datasets involve extraction of virtual movement data from modalities such as mocap [37, 72] and videos [44], which contain a large number of participants and diverse set of actions and environments.”
From Contrastive Predictive Coding for Human Activity Recognition (2021) · cites this paper as [72]
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References 4 · Cited by 18
- Authors
- Takeda, S., Okita, T., Lago, P., & Inoue, S.
- Affiliations
- Kyushu Institute of Technology (JP)
- Venue
- Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
- Publisher
- ACM
- Published
- 2018-10-08
- Pages
- 1444-1448
- OpenAlex
- W2898889947
- Cited by
- 18
- References
- 4
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 4 · 2021: 7 · 2020: 2 · 2019: 2
- FWCI
- 0.83
- Type
- conference paper
- Language
- en
- Open access
- Licence
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- activity recognition, acceleration sensors, motion capture, simulators, sensor placement, sampling frequency, classifier evaluation, multi-sensor data
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