Deep Transfer Learning for Cross-domain Activity Recognition

07/20/2018
by   Jindong Wang, et al.
0

Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar K source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.

READ FULL TEXT

page 5

page 6

research
06/26/2018

Cross-position Activity Recognition with Stratified Transfer Learning

Human activity recognition aims to recognize the activities of daily liv...
research
12/25/2017

Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acq...
research
12/18/2022

Graph Neural Network based Child Activity Recognition

This paper presents an implementation on child activity recognition (CAR...
research
11/22/2019

A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features

The ability to infer the intentions of others, predict their goals, and ...
research
08/28/2017

Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning

Active appearance models (AAMs) are a class of generative models that ha...
research
03/29/2019

Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks

Application of intelligent systems especially in smart homes and health-...
research
06/14/2022

Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition

It is expensive and time-consuming to collect sufficient labeled data to...

Please sign up or login with your details

Forgot password? Click here to reset