Abstract
There have been several approaches for wearable fall detection devices during the last twenty years. The majority of technologies relied on machine learning. Although the given findings appear that the issue is practically addressed, critical problems remain about feature extraction and selection. In this research, the constraint of machine learning on feature extraction is addressed by including a hybrid convolutional operation in our proposed deep residual network, called the DeepFall model. The proposed network automatically generates high-level motion signal characteristics that can be utilized to track falls and everyday activities. FallAllD dataset, a publicly available standard dataset for fall detection that gathered motion signals of falls and other events, was utilized to analyze the proposed network. We performed investigations using a 5-fold cross-validation technique to determine overall accuracy and F-measure. The experimental outcomes show that the proposed DeepFall performs better accuracy (95.19%) and F-measure (92.79%) than the state-of-the-art baseline deep learning networks.
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Acknowledgment
This research project was supported by the Thailand Science Research and Innovation fund; the University of Phayao (Grant No. FF65-RIM041); National Science, Research and Innovation (NSRF); and King Mongkut’s University of Technology North Bangkok with Contract No. KMUTNB-FF-66-07.
The authors also gratefully acknowledge the support provided by Thammasat University Research fund under the TSRI, Contract No. TUFF19/2564 and TUFF24/2565, for the project of “AI Ready City Networking in RUN”, based on the RUN Digital Cluster collaboration scheme.
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Mekruksavanich, S., Jantawong, P., Hnoohom, N., Jitpattanakul, A. (2022). Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network. In: Surinta, O., Kam Fung Yuen, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2022. Lecture Notes in Computer Science(), vol 13651. Springer, Cham. https://doi.org/10.1007/978-3-031-20992-5_19
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