RF-net

Ding, Shuya; Chen, Zhe; Zheng, Tianyue; Luo, Jun

doi:10.1145/3384419.3430735

Cited by 110 publications

(12 citation statements)

References 48 publications

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“…MatNet-eCSI [ 45 ] enables effective gesture recognition using only one sample from each activity in the test environment. RF-Net [ 46 ] also utilizes a metric-based meta-learning framework for one-shot gesture recognition. Moreover, RF-Net [ 46 ] adopts a more complex feature extraction method.…”

Section: Related Workmentioning

confidence: 99%

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Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi

Xie,

Li,

Feng

et al. 2024

Sensors

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RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

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Section: Related Workmentioning

confidence: 99%

“…RF-Net [ 46 ] also utilizes a metric-based meta-learning framework for one-shot gesture recognition. Moreover, RF-Net [ 46 ] adopts a more complex feature extraction method. However, these methods focus on one-shot gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi

Xie,

Li,

Feng

et al. 2024

Sensors

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“…The heterogeneous and imperfect nature of RF data, owing to hardware imperfections and multi-path effects, exacerbates this problem. Prior research in RF sensing has sought to address this issue [7,9,11,13,44,48], with many studies resorting to meta-learning or few-shot learning. However, these methods assume access to samples in the target domain, which may not be practical.…”

Section: Related Workmentioning

confidence: 99%

Construct 3D Hand Skeleton with Commercial WiFi

Ji,

Zhang,

Zheng

et al. 2023

Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

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This paper presents HandFi, which constructs hand skeletons with practical WiFi devices. Unlike previous WiFi hand sensing systems that primarily employ predefined gestures for pattern matching, by constructing the hand skeleton, HandFi can enable a variety of downstream WiFi-based hand sensing applications in gaming, healthcare, and smart homes. Deriving the skeleton from WiFi signals is challenging, especially because the palm is a dominant reflector compared with fingers. HandFi develops a novel multi-task learning neural network with a series of customized loss functions to capture the low-level hand information from WiFi signals. During offline training, HandFi takes raw WiFi signals as input and uses the leap motion to provide supervision. During online use, only with commercial WiFi, HandFi is capable of producing 2D hand masks as well as 3D hand poses. We demonstrate that HandFi can serve as a foundation model to enable developers to build various applications such as finger tracking and sign language recognition, and outperform existing WiFi-based solutions. Artifacts can be found: https://github.com/SIJIEJI/HandFi CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Multi-task learning.

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“…However, they require a large amount of labeled data (a few thousand) and many training epochs. Another learning approach, called meta-learning, is proposed to make DNN more sample-efficient [15,29,53,99], requiring only a few samples to adapt/learn new data distributions from a correlated data stream [1,68]. However, existing meta-learning methods often neglect the forgetting problem of the already learned classes as it primarily aims at fast adaptation towards new tasks only [9,19,22,24,30,79,86,93].…”

Section: Introductionmentioning

confidence: 99%

LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms

Kwon,

Chauhan,

Jia

et al. 2023

Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

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Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. This is an important feature when context, actions, and users change. However, enabling CL on resource-constrained embedded systems is challenging due to the limited labeled data, memory, and computing capacity.In this paper, we propose LifeLearner, a hardware-aware meta continual learning system that drastically optimizes system resources (lower memory, latency, energy consumption) while ensuring high accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies to explicitly cope with data scarcity issues and ensure high accuracy, (2) effectively combine lossless and lossy compression to significantly reduce the resource requirements of CL and rehearsal samples, and (3) developed hardware-aware system on embedded and IoT platforms considering the hardware characteristics.As a result, LifeLearner achieves near-optimal CL performance, falling short by only 2.8% on accuracy compared to an Oracle baseline. With respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically reduces the memory footprint (by 178.7×), end-to-end latency by 80.8-94.2%, and energy consumption by 80.9-94.2%. In addition, we successfully deployed LifeLearner on two edge devices and a microcontroller unit, thereby enabling efficient CL on resource-constrained platforms where it would be impractical to run SOTA methods and the far-reaching deployment of adaptable CL in a ubiquitous manner. Code is available at https://github.com/theyoungkwon/LifeLearner. CCS CONCEPTS• Computer systems organization → Embedded and cyber-physical systems; • Human-centered computing → Ubiquitous and mobile computing.

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RF-net

Cited by 110 publications

References 48 publications

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi

Construct 3D Hand Skeleton with Commercial WiFi

LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms

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