Optimal Threshold Selection for Acceleration-Based Fall Detection

Šeketa, Goran; Vugrin, Jurica; Lacković, Igor

doi:10.1007/978-981-10-7419-6_26

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…These methods are based upon traditional techniques for the classi cation of data. Some famous analytical techniques for data processing are: Thresh-Holding [18] [19], Fuzzy Logic [20], Hidden Markov model [21], and Bayesian Filtering [22]. All these techniques are used to classify the falls from non-falls.…”

Section: Methodsmentioning

confidence: 99%

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An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Hussain

Umair

Ehatisham-ul-Haq

et al. 2020

Preprint

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Abstract Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

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Section: Methodsmentioning

confidence: 99%

“…The proposed methodology uses a machine learning approach for the fall detection. SisFall [19], a publicly available dataset, is used for the training and testing of the proposed algorithm on the falling or non-falling activities. There are four major steps of the proposed methodology i.e.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Hussain

Umair

Ehatisham-ul-Haq

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Different features [ 7 ] and classification techniques [ 8 ] have been explored for use in fall detection systems. In our previous work, we analyzed the performance of fall detection systems using different classification techniques: threshold-based classification [ 9 , 10 ] and different Machine Learning (ML) classifiers [ 11 ]. However, only a few research publications so far have focused on the data segmentation stage, although it significantly affects the systems performance in terms of power efficiency and detection accuracy [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Šeketa

Pavlaković

Džaja

et al. 2021

Sensors

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Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

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“…• Fall detection with accelerometer: Razum and Seketa [2] used a acceleration sensor unit Shimmer3 to collect data for fall detection. They applied two simple thresholdbased algorithms consisting of Euler Angle & Sum Vector Magnitude (THETA&SVM) and SVM&THETA.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Techniques to Optimize Fall Detection Algorithms in Smart Wristband

Zhang²,

Zhou³

et al. 2019

2019 25th International Conference on Automation and Computing (ICAC)

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The consumer electronics market is already saturated with wearable devices that intend to be used to detect falls and request help from carers or family members. However, these products have a high rate of false alarms which affect their reliable performance. To provide the high accuracy and high precision of fall detection for the elderly, this paper presents a machine learning approach to improve the fall detection accuracy and reduce the false alarms. Three machine learning algorithms are deployed in this research, namely the K-Means, Perceptron Neural Network (PNN), and Convolutional Neural Network (CNN) algorithms. A development board with a 9-axis inertial sensor unit is used as a prototype of wristband to collect data and identify falls from seven daily activities. These data is then used to train and test machine learning algorithms. Experimental results show that the CNN algorithm achieves the highest accuracy comparing with K-mean, PNN and the algorithm used in the existing wristbands.

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Optimal Threshold Selection for Acceleration-Based Fall Detection

Cited by 7 publications

References 12 publications

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Using Machine Learning Techniques to Optimize Fall Detection Algorithms in Smart Wristband

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