Deep Learning for Radio-Based Human Sensing: Recent Advances and Future Directions

Nirmal, Isura; Khamis, Abdelwahed; Hassan, Mahbub; Hu, Wen; Zhu, Xiaoqing

doi:10.1109/comst.2021.3058333

Cited by 59 publications

(19 citation statements)

References 126 publications

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“…The authors also provided an overview of the main issues and associations between human activity and anomalous behavior. Nirmal et al [19] presented a complete review and taxonomy of DL studies for RFbased human sensing by comparing different types of algorithms and offering a more detailed view of the DL for human-centric RF-based sensing. They also reviewed 20 released benchmarks of labeled radio signals of human activities.…”

Section: B Surveys On Device-independent Approachesmentioning

confidence: 99%

“…The idea that learning in the current time step does not depend only on the historical information but also on the future information motivated the design of bidirectional RNNs (Bi-RNNs) that process the input sequences both forward and backward directions. This encouraged the usage of bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU), which have been showing great success for a wide variety of applications [19].…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

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Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

et al. 2021

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The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.

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Section: B Surveys On Device-independent Approachesmentioning

confidence: 99%

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

et al. 2021

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“…A. Overview W I-FI sensing has recently received extensive research interests [1], [2]. Wi-Fi is ubiquitous as it has been equipped in many consumable electronics including laptops, smartphones, tablets, wearable devices such as Fitbits, and smart home appliances, to name but a few.…”

Section: Introductionmentioning

confidence: 99%

FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning

Yin¹,

Zhang²,

Shen³

et al. 2022

Preprint

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Wi-Fi sensing can classify human activities because each activity causes unique changes to the channel state information (CSI). Existing WiFi sensing suffers from limited scalability as the system needs to be retrained whenever new activities are added, which cause overheads of data collection and retraining. Cross-domain sensing may fail because the mapping between activities and CSI variations is destroyed when a different environment or user (domain) is involved. This paper proposed a few-shot learning-based WiFi sensing system, named FewSense, which can recognise novel classes in unseen domains with only few samples. Specifically, a feature extractor was pre-trained offline using the source domain data. When the system was applied in the target domain, few samples were used to fine-tune the feature extractor for domain adaptation. Inference was made by computing the cosine similarity. FewSense can further boost the classification accuracy by collaboratively fusing inference from multiple receivers. We evaluated the performance using three public datasets, i.e., SignFi, Widar, and Wiar. The results show that FewSense with five-shot learning recognised novel classes in unseen domains with an accuracy of 90.3%, 96.5% ,82.7% on SignFi, Widar, and Wiar datasets, respectively. Our collaborative sensing model improved system performance by an average of 30%.

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“…In these applications, fundamental differences in the data domain have driven the development of unique AI-based signal processing. In particular, sensing data is not inherently acquired as images, but mostly as complex time series with amplitudes and phases related to the electromagnetic scattering and kinematics of the sensed targets [21]. Hence, some preprocessing stages, e.g., filtering and time-frequency analysis, are required to transform the raw signals into the data samples as the inputs of ML models.…”

Section: Introductionmentioning

confidence: 99%

Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

Liu¹,

Zhu²,

Wang³

et al. 2022

Preprint

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With the breakthroughs in deep learning and contactless sensors, the recent years have witnessed a rise of ambient intelligence applications and services, spanning from healthcare delivery to intelligent home. Federated edge learning (FEEL), as a privacy-enhancing paradigm of collaborative learning at the network edge, is expected to be the core engine to achieve ambient intelligence. Sensing, computation, and communication (SC 2 ) are highly coupled processes in FEEL and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at the edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resource jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process; the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting the derived relation between different control variables (resources),

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Deep Learning for Radio-Based Human Sensing: Recent Advances and Future Directions

Cited by 59 publications

References 126 publications

Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning

Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

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