Human fall detection on embedded platform using depth maps and wireless accelerometer
Abstract
Since falls are a major public health problem in an aging society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.
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The combination of ambient sensors with wearables data has the potential to improve the reliability of fall detection systems while keeping a low false alarm rate.
“For example, the combination of ambient sensors (such as depth cameras and microphones) with wearables data (for example, accelerometers, which measure physical activity) has the potential to improve the reliability of fall detection systems while keeping a low false alarm rate 53 ,”
From Multimodal biomedical AI (2022) · cites this paper as [53]
The integration of data from multiple modalities and sensors represents a promising opportunity to improve remote patient monitoring, and some studies have already demonstrated the potential of multimodal data in these scenarios.
“The integration of data from these multiple modalities and sensors represents a promising opportunity to improve remote patient monitoring, and some studies have already demonstrated the potential of multimodal data in these scenarios.”
From Multimodal biomedical AI (2022) · cites this paper as [53]
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“This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [32], Ugulino [33], USC HAD [34], DaLiAc [10], EvAAL [35], MHEALTH [36], UCI ARSA [27], BaSA [37], UR Fall Detection [38], MMsys [9], SisFall [39], UMA Fall 4 [18], and REALDISP [40].”
Background
- Authors
- Kwolek, B., & Kepski, M.
- Affiliations
- AGH University of Krakow (PL)
- Corresponding author
- Kwolek, Bogdan
- ORCID
- Kwolek, Bogdan
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 117
- Issue
- 3
- Pages
- 489-501
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2074099390
- Cited by
- 650
- References
- 31
- Citations by year
- 2026: 51 · 2025: 71 · 2024: 97 · 2023: 78 · 2022: 75 · 2021: 59 · 2020: 71 · 2019: 49 · 2018: 38 · 2017: 39 · 2016: 17 · 2015: 5
- FWCI
- 18.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Centre
- 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 › Gait Recognition and Analysis
- Keywords
- fall detection, depth images, wireless accelerometers, embedded systems, low-cost wearable sensors, false alarm reduction, support vector machine classification, human detection, non-intrusive monitoring, elderly care, privacy preservation
- MeSH terms
- Accidental Falls, Activities of Daily Living, Adult, Facility Design and Construction, Humans, Middle Aged, Movement, Reproducibility of Results, Monitoring, Ambulatory, Support Vector Machine, Accelerometry
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