Real-Time classification of various types of falls and activities of daily livings based on CNN LSTM network

Kadhum Al-Majdi, Raed S.H. AL-Musawi, Ali H. Ali, Saif Ali Abudlmoniem, Yaqeen S. Mezaal

Abstract


In this research, two multiclass models have been developed and implemented, namely, a standard long-short-term memory (LSTM) model and a Convolutional neural network (CNN) combined with LSTM (CNN-LSTM) model. Both models operate on raw acceleration data stored in the Sisfall public dataset. These models have been trained using the TensorFlow framework to classify and recognize among ten different events: five separate falls and five activities of daily livings (ADLs). An accuracy of more than 96% has been reached in the first 200 epochs of the training process. Furthermore, a real-time prototype for recognizing falls and ADLs has been implemented and developed using the TensorFlow lite framework and Raspberry PI, which resulted in an acceptable performance.

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DOI: http://dx.doi.org/10.21533/pen.v9i3.2599

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Copyright (c) 2021 Kadhum Al-Majdi, Raed S.H. AL-Musawi, Ali H. Ali, Saif Ali Abudlmoniem, Yaqeen S. Mezaal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License