Identity and gender recognition using a capacitive sensing floor and neural networks

HIGHLIGHTS

• who: Daniel Konings et al. from the Department of Mechanical and Electrical Engineering (MEE), School of Food and Advanced Technology (SF and AT), Massey University, Auckland, New Zealand have published the article: Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks, in the Journal: Sensors 2022, 22, x FOR PEER REVIEW of /2022/
• what: The authors demonstrate that the Bi-directional Long ShortTerm Memory (BLSTM)-based algorithm is the most accurate for subject identification, attaining an accuracy of 98.12%.
• future: This is another avenue of future research that the . . .
  

   

  

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