RadioSense - Wireless Big Data Augmented Smart Industry

Projektin yksityiskohdat


In manufacturing environments, human workers interact with increasingly autonomous machinery. To ensure workspace safety and production efficiency during human-robot (HR) cooperation (HRC), continuous and accurate tracking and perception of workers' activities is the key. Examples are reactive collision-prevention, active recognition of workers' intention, as well as accurate tracking of human-robot interactions. The project moves forward the state-of-the-art in advanced sensing and perception for next generation manufacturing workspace. We explore passive radio sensing to track, recognize and analyse HRC without requiring workers to wear devices, and without the need for privacy-intrusive video, while ensuring workers' safety and privacy. RadioSense leverages real-time collection and processing of heterogeneous radio signal streams (e.g., 4G/5G and WiFi connections) and multiple Channel State Information (CSI) between different links/antennas.
Todellinen alku/loppupvm01/01/201928/02/2022


Tutustu tutkimuksen aiheisiin, joita tämä projekti koskee. Nämä merkinnät luodaan taustalla olevien stipendien/apurahojen perusteella. Yhdessä ne muodostavat ainutlaatuisen sormenjäljen.
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    Salami, D., Palipana, S., Kodali, M. & Sigg, S., syysk. 2020, Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE, 6 Sivumäärä 9231569. (IEEE International Workshop on Machine Learning for Signal Processing).

    Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

    Open access
    4 Sitaatiot (Scopus)
    118 Lataukset (Pure)
  • Extracting Human Context Through Receiver-End Beamforming

    Palipana, S. & Sigg, S., 2019, julkaisussa: IEEE Access. 7, s. 154535-154545 11 Sivumäärä, 8856202.

    Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

    Open access
    3 Sitaatiot (Scopus)
    34 Lataukset (Pure)
  • Receiver-side beamforming to isolate channel perturbations from a human target in a device-free setting

    Palipana, S. & Sigg, S., 10 marrask. 2019, DFHS 2019 - Proceedings of the 1st ACM Workshop on Device-Free Human Sensing. ACM, s. 6-9 4 Sivumäärä

    Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu