Locating Everyday Objects using NFC Textiles
Published in The ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), 2021
Recommended citation: Jingxian Wang (Co-Primary), Junbo Zhang (Co-Primary), Ke Li, Chengfeng Pan, Carmel Majidi, and Swarun Kumar. 2021. Locating Everyday Objects using NFC Textiles. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) (IPSN). Association for Computing Machinery, New York, NY, USA, 15–30. https://dl.acm.org/doi/10.1145/3412382.3458254
Best Paper Award & Best Presentation Award
May 18–21, 2021, Nashville, Tennessee, USA
Keyword: Near Field Communication, Localization, Textile Coils, Beamforming
This paper builds a Near-field Communication (NFC) based localization system that allows ordinary surfaces to locate surrounding objects with high accuracy in the near-field. While there is rich prior work on device-free localization using far-field wireless technologies, the near-field is less explored. Prior work in this space operates at extremely small ranges (a few centimeters), leading to designs that sense close proximity rather than location.
We propose TextileSense, a near-field beamforming system which can track everyday objects made of conductive materials (e.g., a human hand) even if they are a few tens of centimeters away. We use multiple flexible NFC coil antennas embedded in ordinary and irregularly shaped surfaces we interact with in smart environments - furniture, carpets, etc. We design and fabricate specialized textile coils woven into the fabric of the furniture and easily hidden by acrylic paint. We then develop a near-field blind beamforming algorithm to efficiently detect surrounding objects, and use a data-driven approach to further infer their location. A detailed experimental evaluation of TextileSense shows an average accuracy of 3.5 cm in tracking the location of objects of interest within a few tens of centimeters from the furniture.