A simply fall-detection algorithm using accelerometers on a smartphone
Abstract
Every year, several thousands of elderly people experience with falling accident. Falling is then a main problem about healthiness of elders. This paper tries to find out a simple algorithm to detect a fall. With less calculation, the device can quickly distinguish between a fall and a normal activity of daily living (ADL). As the smartphone technology is currently in very advance, it includes several sensors to come along. The sensors building in the smartphone are very useful in every field of measurements even in medical engineering. The tri-axial accelerometer is one sensor available on the smartphone and one application is to use for fall detection. From the study, the simple algorithm can be applied for fall detection by observing any change of x-, y-, or z-acceleration 10g within time limited obtaining from ADLs in terms of lying down. The advantages of using the smartphone as a fall detector are that it can alarm or call out for help. It is also getting cheap, widely used, and comfortable to use or mount.
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Various sensors such as camera sensor, microphone sensor, accelerometer sensor, and gyroscope sensor were used in healthcare-based applications.
“Various sensors such as camera sensor [53–55], microphone sensor [56–58], accelerometer sensor [59–61], and gyroscope sensor [59, 62] were used in the healthcare-based applications.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [61]
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khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works
- Authors
- Thammasat, E., & Chaicharn, J.
- Affiliations
- Thailand Institute of Scientific and Technological Research (TH)
- Thammasat University (TH)
- Venue
- The 5th 2012 Biomedical Engineering International Conference
- Publisher
- IEEE
- Published
- 2012-12
- Pages
- 1-4
- OpenAlex
- W2025348589
- Cited by
- 27
- References
- 0
- Citations by year
- 2025: 2 · 2024: 1 · 2021: 1 · 2020: 2 · 2019: 6 · 2018: 1 · 2017: 2 · 2016: 4 · 2015: 4 · 2014: 4
- FWCI
- 1.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Biomedical Engineering International Conference (publishedVersion)
- 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, smartphone accelerometer, triaxial accelerometer, falls in older adults, activities of daily living, low-complexity algorithms, fall alarm
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