2023
DOI: 10.1016/j.inffus.2023.101890
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FL-FD: Federated learning-based fall detection with multimodal data fusion

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Cited by 38 publications
(3 citation statements)
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“…However, due to the large number of wavepoints (1845) in the original NIR spectra of maize seeds, each training iteration with the one-dimensional convolutional kernel contains less data, which can potentially result in slow network training. In contrast, three-dimensional images have the capability to comprehensively extract spectral information reflecting substances [25], offering advantages such as fast and accurate spectral information processing [26]. Considering these factors, this study employs GAF to transform the one-dimensional spectral data vector into a three-dimensional spectral information matrix in order to meet the requirements of the convolutional layer effectively.…”
Section: Near-infrared Spectral Feature Map Conversionmentioning
confidence: 99%
“…However, due to the large number of wavepoints (1845) in the original NIR spectra of maize seeds, each training iteration with the one-dimensional convolutional kernel contains less data, which can potentially result in slow network training. In contrast, three-dimensional images have the capability to comprehensively extract spectral information reflecting substances [25], offering advantages such as fast and accurate spectral information processing [26]. Considering these factors, this study employs GAF to transform the one-dimensional spectral data vector into a three-dimensional spectral information matrix in order to meet the requirements of the convolutional layer effectively.…”
Section: Near-infrared Spectral Feature Map Conversionmentioning
confidence: 99%
“…In recent years, numerous studies have investigated FL-based HAR. For instance, the authors in [13] proposed a multimodal data fusion approach for fall detection in an FL environment. The time-series data from wearable sensors are initially transformed into images using the gramian angular field method.…”
Section: Federated Learning-based Harmentioning
confidence: 99%
“…The process of labelling vast numbers of data is labour-intensive and often requires domain expertise [3]. Furthermore, the centralised nature of HAR systems poses significant communication and storage costs, especially when transmitting high-dimensional raw data [13]. Additionally, processing this data in centralised servers can incur additional latency, especially when dealing with real-time activity recognition tasks.…”
Section: Introductionmentioning
confidence: 99%