A Tensor Approach for Activity Recognition and Fall Detection Using Wearable Inertial Sensors

Boutellaa, Elhocine; Ghanem, Khalida; Tayakout, Hakim; Kerdjidj, Oussama; Harizi, Farid; Bourennane, Salah

doi:10.1109/smart-tech49988.2020.00054

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…In this work, we make use of the SisFall dataset to perform fall detection with direction and severity and activity of daily living detection since it has been the dataset of choice in multiple works addressing the fall detection domain [ 37 , 38 , 39 ] as it includes recordings of volunteers from various ages (ages from 19 to 75 years), has diversity in the gender make up of the participants (19 males and 19 females from a total of 38 volunteers) and is one of the biggest datasets available in terms of the type of falls and activities being recorded. Since both accelerometers are placed at the same position and therefore measure the same movements, data from only one of the accelerometers along with the gyroscope are considered in this work.…”

Section: Datamentioning

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: Datamentioning

confidence: 99%