Radar for assisted living in the context of Internet of Things for Health and beyond

EasyChair Preprint 465

Abstract

This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare.

Keyphrases: Assisted Living, Human Activity Classification, classification accuracy, data fusion, fall detection, feature extraction, feature selection, machine learning, radar sensors, radar signal processing

@booklet{EasyChair:465,
  author    = {Julien LeKernec and Francesco Fioranelli and Shufan Yang and Jordane Lorandel and Olivier Romain},
  title     = {Radar for assisted living in the context of Internet of Things for Health and beyond},
  doi       = {10.29007/tznx},
  howpublished = {EasyChair Preprint 465},
  year      = {EasyChair, 2018}}