Joint Motion Classification and Person Identification via Multitask Learning for Smart Homes

Lang, Yue; Wang, Qing; Yang, Yang; Hou, Chunping; Li, Haiping; He, Yuan

doi:10.1109/jiot.2019.2929833

Cited by 32 publications

(8 citation statements)

References 55 publications

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“…The security and privacy of data captured by sensors [ 103 , 104 ], the durability of power supplies (batteries) [ 91 , 105 ], the resilience of communications [ 105 , 106 ], and the intrusiveness of sensors [ 103 , 107 ], among other NFRs, must be considered at each stage of an IoTS development. Therefore, for example, there are research lines on different methods, protocols, and guidelines to guarantee data security and privacy [ 30 , 108 , 109 , 110 ], low energy consumption [ 110 , 111 , 112 , 113 ], or to integrate sensors with different levels of intrusiveness [ 114 , 115 ], among others.…”

Section: Methodologies Designed For the Development Of Iotssmentioning

confidence: 99%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose.

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Section: Methodologies Designed For the Development Of Iotssmentioning

confidence: 99%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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“…The proposed algorithms include conventional machine learning based on feature engineering combined with appropriate classifiers, such as support vector machines (SVM), decision trees (DT), random forest (RF), and empowerment methods. They also include automatic feature learning such as deep learning (e.g., deep belief networks (DBN), autoencoder (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative antagonist networks (GAN)) (Lang et al, 2019). In general, it is now recognized that a higher degree of automation improves radar environmental awareness, irrespective of the type of application considered.…”

Section: Machine Learning For Radarmentioning

confidence: 99%

“…Modern high-resolution radars, equipped with advanced signal processing algorithms, have a better capability to extract micro-Doppler features, which allow classical problems such as non-cooperative target detection and classification to be solved in a more efficient way (Clemente et al, 2015;Ritchie et al, 2016;Fioranelli et al, 2020). This also paves the way for new applications, such as human activity monitoring (Amin, 2017;Shrestha et al, 2020), urban and indoor surveillance (Pastina et al, 2015;Seyfioglu et al, 2018), healthcare (Li et al, 2018a;Lang et al, 2019;Seifert et al, 2019), automotive applications (Khomchuk et al, 2016;Duggal et al, 2020), and manufacturing (Zeintl et al, 2019;Izzo et al, 2020). A recent book (Fioranelli et al, 2020) covered the latest developments in radar micro-Doppler signatures and noncooperative recognition of moving targets and identified a number of ongoing research areas, among which passive radar approaches for healthcare, multimodal sensing for assisted living using radar, small drones and bird signatures extraction, and micro-Doppler signature extraction and analysis for automotive application were mentioned.…”

Section: Micro-doppler Radarmentioning

confidence: 99%

Grand Challenges in Radar Signal Processing

Gini

2021

Front. Signal Process.

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“…However, for both of the tasks, MRA-Net converges slightly slower than the other baselines, which is probably due to the parameter optimization complexity caused by MTL. Subsequently, we compared the proposed MRA-Net with another MTL network, JMI-CNN [32], for the person identification and activity classification tasks. Additionally, to more comprehensively compare the two networks, Gaussian white noise (GWN) with different signal noise ratios (SNRs) was added on the MD signatures of the dataset.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

Jing

2019

Remote Sensing

Self Cite

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Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received more attention due to radar’s robustness to the environment and low power consumption. Activity recognition and person identification are generally treated as separate problems. However, designing different networks for these two tasks brings a high computational complexity and wastes of resources to some extent. Furthermore, there are some correlations in activity recognition and person identification tasks. In this work, we propose a multiscale residual attention network (MRA-Net) for joint activity recognition and person identification with radar micro-Doppler signatures. A fine-grained loss weight learning (FLWL) mechanism is presented for elaborating a multitask loss to optimize MRA-Net. In addition, we construct a new radar micro-Doppler dataset with dual labels of activity and identity. With the proposed model trained on this dataset, we demonstrate that our method achieves the state-of-the-art performance in both radar-based activity recognition and person identification tasks. The impact of the FLWL mechanism was further investigated, and ablation studies of the efficacy of each component in MRA-Net were also conducted.

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Joint Motion Classification and Person Identification via Multitask Learning for Smart Homes

Cited by 32 publications

References 55 publications

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Grand Challenges in Radar Signal Processing

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

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