The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
UP-Fall Detection Dataset: A Multimodal Approach
Open accessAbstract
Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.
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Cites
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“The latter approach is a methodology that allows calculating the apparent displacements of objects in an image sequence, these displacements, in general, are associated with brightness variations and can give correspondence information between the pixels of consecutive images [41].”
BackgroundA survey on fall detection: Principles and approaches
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Cited by
Elderly Fall Detection Systems: A Literature Survey
Wang, X., Ellul, J., & Azzopardi, G. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00071 · Cited by 380
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things
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Human Activity Recognition Data Analysis: History, Evolutions, and New Trends
Ariza-Colpas, P. P., Vicario, E., Oviedo-Carrascal, A. I., Butt Aziz, S., Piñeres-Melo, M. A., Quintero-Linero, A., & Patara, F. (2022) · Sensors · doi:10.3390/s22093401 · Cited by 52
Dataset for human fall recognition in an uncontrolled environment
Guerrero, J. C. E., España, E. M., Añasco, M. M., & Lopera, J. E. P. (2022) · Data in Brief · doi:10.1016/j.dib.2022.108610 · Cited by 45
Yan, J., Wang, X., Shi, J., & Hu, S. (2023) · Sensors · doi:10.3390/s23042153 · Cited by 28
Metadata
- Authors
- Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort-Asturiano, C.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-04-28
- Volume
- 19
- Issue
- 9
- Pages
- 1988
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2941398656
- Cited by
- 403
- References
- 41
- FWCI
- 16.76
- Type
- journal article
- Language
- en
- Open access
- gold
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- Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort-Asturiano, C. (2019). UP-Fall Detection Dataset: A Multimodal Approach. Sensors. https://doi.org/10.3390/s19091988
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