2018
DOI: 10.3390/s18041101
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Abstract: The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In thi… Show more

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Cited by 81 publications
(42 citation statements)
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“…The achieved results should be accepted with caution because the experimental dataset used for the classifier training is relatively small and acquired only for young volunteers. Nevertheless, in [42], it is shown that datasets for young people can be used instead of the one for elderly people while training a fall classifier.…”
Section: Discussionmentioning
confidence: 99%
“…The achieved results should be accepted with caution because the experimental dataset used for the classifier training is relatively small and acquired only for young volunteers. Nevertheless, in [42], it is shown that datasets for young people can be used instead of the one for elderly people while training a fall classifier.…”
Section: Discussionmentioning
confidence: 99%
“…Three thresholds for resultant angular velocity, resultant angular acceleration, and resultant angle change were set to distinguish falls from activities of daily living. Sucerquia et al [24] presented a novel fall detection approach based on a Kalman filter and a non-linear classification feature, using data from a tri-axial accelerometer. Their methodology required a low sampling frequency of only 25 Hz.…”
Section: Related Workmentioning
confidence: 99%
“…Shi et al [27] uses J48 decision tree, which is an efficient algorithm derived from C4.5 decision tree [28], to detect falls. The aforementioned methods [22,23,24,25,26,27] do not consider the effect of personalization on fall detection. Medrano et al [8] evaluated four algorithms (nearest neighbor (NN), local outlier factor (LOC), one-class support vector machine (One-Class SVM), and SVM) after personalization used as fall detectors to boost their performance when compared to their non-personalized versions.…”
Section: Related Workmentioning
confidence: 99%
“…The use of smart technology applied to the human movement/exercise analysis has been a must for researchers, clinicians, practitioners, and patients. Inertial measurement unit technology is a less time consuming, noninvasive, and practical alternative to the video-based methods used by researchers to analyze human motion, allowing to detect with a higher precision several parameters during exercise [1,2]. Technology advanced in a way that due to the sensor' compactness (small size) multiple applications may be used in human locomotion, including exercise/sports movements [3,4].…”
Section: Introductionmentioning
confidence: 99%