Privacy Preserving Multi-Class Fall Classification Based on Cascaded Learning and Noisy Labels Handling

EasyChair Preprint 8247

Abstract

With an increasingly ageing population in the world, fall detection and classification for elderly people becomes an imperative problem that needs to be addressed for assisted living. Currently, most of the fall detection algorithms are based on wearable and non-wearable sensors, such as based on accelerometer and video camera respectively. In this work, different from previous vision-based methods where the whole images are used, to mitigate the privacy protection problem and detect different types of fall events, we utilize only the skeleton data to achieve the classification of different fall events by using a deep neural network (DNN). The cost of manually labelling and due to varieties of annotators, for a recorded dataset, there always exist errors which will deteriorate the performance. To address this issue, we introduce the confident learning to remove wrong labelled samples and propose a new cascaded learning method to solve the noisy labelled data problem. To confirm the efficacy of the proposed method, we compare different algorithms on the UP-Fall dataset to show that the proposed method performs better than the state-of-the-art.

Keyphrases: Cascaded Learning, Noisy label, Skeleton features, confident learning, fall detection

@booklet{EasyChair:8247,
  author    = {Leiyu Xie and Yang Sun and Jonathon Chambers and Mohsen Naqvi},
  title     = {Privacy Preserving Multi-Class Fall Classification Based on Cascaded Learning and Noisy Labels Handling},
  howpublished = {EasyChair Preprint 8247},
  year      = {EasyChair, 2022}}