Sensing finger input using an RFID transmission line

RFID techniques have been extensively used in sensing systems due to their low cost. However, limited by the structural simplicity, collision is one key issue which is inevitable in RFID systems, thus limiting the accuracy and scalability of such sensing systems. Existing anti-collision techniques try to enable parallel decoding without sensing based applications in mind, which can not operate on COTS RFID systems. To address the issue, we propose COFFEE, which enables parallel channel estimation of COTS passive tags by harnessing the collision. We revisit the physical layer design of current standard. By exploiting the characteristics of low sampling rate and channel diversity of RFID tags, we separate the collided data and extract the channels of the collided tags. We also propose a tag identification algorithm which explores history channel information and identify the tags without decoding. COFFEE is compatible with current COTS RFID standards which can be applied to all RFID-based sensing systems without any modification on tag side. To evaluate the real world performance of our system, we build a prototype and conduct extensive experiments. The experimental results show that we can achieve up to 7.33x median time resolution gain for the best case and 3.42x median gain on average.

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“…RFID-based sensing systems [10,28,33,36] receive a growing interest. Some recent works propose motion tracking systems [5,18].…”

Section: Related Workmentioning

confidence: 99%

Embracing Collisions to Increase Fidelity of Sensing Systems with COTS Tags

Sun

Srinivasan

2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Wireless signals like WiFi, RFID and (ultra)sound are emerging as a powerful modality for ubiquitous sensing [6,21,22,27,37,41,47,50,56,77,88,94]. Indeed, wireless sensing now underpins many emerging applications, ranging from smart home personalization [16,24] and fall monitoring [95,99,100] to emotion detection [98] and vital sign monitoring [14,36,44,54,63,73].…”

Section: Introductionmentioning

confidence: 99%

Rise

Zhai

Tang

Nurmi

et al. 2021

Proceedings of the 27th Annual International Conference on Mobile Computing and Networking

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Wireless sensing builds upon machine learning shows encouraging results. However, adopting wireless sensing as a large-scale solution remains challenging as experiences from deployments have shown the performance of a machine-learned model to suffer when there are changes in the environment, e.g., when furniture is moved or when other objects are added or removed from the environment. We present Rise, a novel solution for enhancing the robustness and performance of learning-based wireless sensing techniques against such changes during a deployment. Rise combines probability and statistical assessments together with anomaly detection to identify samples that are likely to be misclassified and uses feedback on these samples to update a deployed wireless sensing model. We validate Rise through extensive empirical benchmarks by considering 11 representative sensing methods covering a broad range of wireless sensing tasks. Our results show that Rise can identify 92.3% of misclassifications on average. We showcase how Rise can be combined with incremental learning to help wireless sensing models retain their performance against dynamic changes in the operating environment to reduce the maintenance cost, paving the way for learning-based wireless sensing to become capable of supporting long-term monitoring in complex everyday environments. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.

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Eliminating Design Effort: A Reconfigurable Sensing Framework For Chipless, Backscatter Tags

Jiao

et al. 2022

2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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Sensing finger input using an RFID transmission line

Cited by 14 publications

References 18 publications

Embracing Collisions to Increase Fidelity of Sensing Systems with COTS Tags

Embracing Collisions to Increase Fidelity of Sensing Systems with COTS Tags

Rise

Eliminating Design Effort: A Reconfigurable Sensing Framework For Chipless, Backscatter Tags

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