It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way.
1 code implementation • 8 Oct 2021 • Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise.
On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet.
We integrate WiFi transmission signals with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics, and the sensor parameters.
However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms.
For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems.