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Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices
Open accessAbstract
Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. A fall detection system aims to detect a fall as soon as it occurs, therefore issuing an automatic assistance request. Wearable embedded sensors are emerging as the most viable solution for continuous monitoring since they are more effective, less intrusive and less expensive than other systems. Tailoring a deep learning method to the requirements of microcontrollers entails matching very stringent constraints in terms of both memory and computational power. In addition, datasets acquired with wearable devices are relatively scarce and not necessarily devised for supervised learning. In this work, we discuss the design of a software architecture based on recurrent neural networks which can be effective for fall detection while running entirely onboard a wearable device. The well-known and publicly-available SisFall dataset was adopted and extended with fine-grained temporal annotations to cope with the supervised training of recurrent neural networks. We then show that an appropriate process of architectural minimization together with accurate hyperparameters selection leads to a workable model which compares favorably with other detection techniques. The embedding of the resulting architecture has been validated using a state-of-art hardware device.
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“Unlike static classifiers, LSTMs maintain an internal memory state, allowing the model to distinguish between a “controlled descent” and a “sudden drop” based on the velocity history of the movement [18].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [18]
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“Musci et al. [21] have utilized a graphical annotation tool for assigning the temporal labels to fall activity data for the SisFall dataset, but again it was a manual approach and required a team of experts to perform the task.”
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Metadata
- Authors
- Musci, M., De Martini, D., Blago, N., Facchinetti, T., & Piastra, M.
- Affiliations
- University of Pavia (IT)
- Venue
- IEEE Transactions on Emerging Topics in Computing
- Published
- 2021-07-01
- Volume
- 9
- Issue
- 3
- Pages
- 1276-1289
- ISSN
- 2168-6750 (ISSN-L)
- 2376-4562
- OpenAlex
- W2797186212
- Cited by
- 128
- References
- 56
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 25 · 2023: 19 · 2022: 21 · 2021: 19
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Emerging Topics in Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- Regione Lombardia (139625)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Keywords
- fall detection, wearable sensors, recurrent neural networks, embedded deep learning, microcontroller, SisFall dataset, temporal annotation, model compression, hyperparameter optimization, software architecture
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