Authors:
Nassim Mokhtari
;
Alexis Nédélec
;
Marlène Gilles
and
Pierre De Loor
Affiliation:
Lab-STICC (CNRS UMR 6285), ENIB, Centre Européen de Réalité Virtuelle, Brest, France
Keyword(s):
3D Skeleton Data, Image Encoding, Online Human Activity Recognition, Deep Learning, Neural Architecture Search, FireFly Algorithm, NAS-BENCH-101, Efficiency Estimation, Energy Consumption.
Abstract:
Human activity recognition using sensor data can be approached as a problem of classifying time series data. Deep learning models allow for great progress in this domain, but there are still some areas for improvement. In addition, the environmental impact of deep learning is a problem that must be addressed in today’s machine learning studies. In this research, we propose to automate deep learning model design for human activity recognition by using an existing training-free Neural Architecture Search method. By this way, we decrease the time consumed by classical NAS approaches (GPU based) by a factor of 470, and the energy consumed by a factor of 170. Finally, We propose a new criterion to estimate the relevance of a deep learning model based on a balance between both performance and computational cost. This criterion allows to reduce the size of neural architectures by preserving its capacity to recognize human activities.