Real time direction-sensitive fall detection system using accelerometer and learning classifier

Hossain, Shaikh Farhad; Islam, Md. Zahurul; Ali, Md. Liakot

doi:10.1109/icaee.2017.8255335

Cited by 4 publications

(1 citation statement)

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“…A different take on post fall intelligence is the determination of direction in falls. Direction aware fall detection has been performed by Hossain et al in [ 22 , 23 ] where they include ADLs along with direction sensitive fall detection using an accelerometer. In their work, they use various statistical features from the data collected by them along with an SVM classifier to different between five different classes, ADL, Forward Fall, Backward Fall, Right Fall and Left Fall.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling

Syed

Sierra-Sosa

Kumar

et al. 2021

Sensors

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Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems.

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Section: Literature Reviewmentioning

confidence: 99%