A federated learning system with enhanced feature extraction for human activity recognition

Xiao, Zhiwen; Xu, Xin; Xing, Huanlai; Song, Fuhong; Wang, Xinhan; Zhao, Bowen

doi:10.1016/j.knosys.2021.107338

Cited by 168 publications

(64 citation statements)

References 48 publications

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“…In the stage of bar and segment feature extraction, Zheng and others proposed a dynamic bar pointer model based on Bayesian theory, which can distinguish the bar position of music, but it is only applicable to MIDI music with relatively constant music speed [8]. Xiao and others' sequential Monte Carlo method based on Bayesian theory can extract bar information, but it is only applicable to single-tone audio music [9]. Goux and others proposed a method of dividing music segments by using the similarity between bars, which judges the similarity of bars by calculating the cosine of the vector including the angle between adjacent small knots, to divide the music into multiple independent segments [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Luo

2022

Mobile Information Systems

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Aiming at the complex and changeable characteristics of intelligent singing skills in the context of Internet of Things, this paper proposes a feature extraction method suitable for intelligent singing skills in this context. Firstly, focusing on vocal features, the time-domain algorithm based on open-loop and closed-loop gene extraction extracts the genetic features of songs with accompaniment; then, the section and its features are extracted by using the windowed moving matching algorithm, and the segments are divided by using the similarity between adjacent segments to obtain the segment features with emotional factors. The segment features are input into the improved BP emotion recognizer for emotion recognition. Finally, the intelligent singing skills of the whole music are determined. The experimental results show that, with the increase in feature extraction time, the accuracy of the extraction results of the existing methods changes little, which is basically maintained at a low level between 15% and 30%. When the proposed method is for feature extraction of intelligent singing skill information, the accuracy shows a continuous growth trend, and with the growth of time, its accuracy is significantly higher than the existing methods, indicating that the proposed method has significant advantages in the accuracy of feature extraction. Because this waveform feature extraction method is applied to the intelligent singing skills under the background of the Internet of Things, it has the advantages of high extraction efficiency, high accuracy, and reliability.

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Section: Literature Reviewmentioning

confidence: 99%

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Luo

2022

Mobile Information Systems

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“…It can enable the collaborative learning of a global model without the need to expose users' raw data. Xiao et al [279] realized a federated averaging method combined with a perceptive extraction network to improve the performance of the federated learning system. Tu et al [280] designed a dynamic layer sharing scheme, which assisted the merging of local models to speed up the model convergence and achieved dynamic aggregation of models.…”

Section: Privacy Protectionmentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

216

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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“…For example, the classification tasks of COVID-19 detections [71][72][73][74][75][76][77][78][79][80][81][82][83][84], cancer diagnoses [85][86][87][88][89][90][91][92][93][94] and autism spectrum disorder (ASD) [84,[95][96][97] are considered in a FL setting in healthcare. Other classification tasks studied include emotion recognition and human activity [98][99][100] and prediction of patient hospitalization [101,102]. Table 1 provides an overview of the classification tasks used in FL research for medical applications.…”

Section: Healthcarementioning

confidence: 99%

Federated learning: Applications, challenges and future directions

Bharati

Mondal

Podder

et al. 2022

HIS

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Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions.

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A federated learning system with enhanced feature extraction for human activity recognition

Cited by 168 publications

References 48 publications

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Federated learning: Applications, challenges and future directions

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