2 research outputs found
Reducing user intervention in incremental activityrecognition for assistive technologies
Activity recognition has recently gained a lot of interest and there already exist several methods to detect human activites based on wearable sensors. Most of the existing methods rely on a database of labelled activities that is used to train an offline activity recognition system. This paper presents an approach to build an online activity recognition system that do not require any a priori labelled data. The system incrementally learns activities by actively querying the user for labels. To choose when the user should be queried, we compare a method based on random sampling and another that uses a Growing Neural Gas (GNG). The use of GNG helps reducing the number of user queries by 20% to 30%
Online learning of personalised human activity recognition models from user-provided annotations
PhD ThesisIn Human Activity Recognition (HAR), supervised and semi-supervised training are
important tools for devising parametric activity models. For the best modelling performance,
large amounts of annotated personalised sample data are typically required.
Annotating often represents the bottleneck in the overall modelling process as it usually
involves retrospective analysis of experimental ground truth, like video footage. These
approaches typically neglect that prospective users of HAR systems are themselves
key sources of ground truth for their own activities.
This research therefore involves the users of HAR monitors in the annotation process.
The process relies solely on users' short term memory and engages with them to
parsimoniously provide annotations for their own activities as they unfold. E ects
of user input are optimised by using Online Active Learning (OAL) to identify the
most critical annotations which are expected to lead to highly optimal HAR model
performance gains.
Personalised HAR models are trained from user-provided annotations as part of the
evaluation, focusing mainly on objective model accuracy. The OAL approach is contrasted
with Random Selection (RS) { a naive method which makes uninformed annotation
requests. A range of simulation-based annotation scenarios demonstrate that
using OAL brings bene ts in terms of HAR model performance over RS. Additionally,
a mobile application is implemented and deployed in a naturalistic context to collect
annotations from a panel of human participants. The deployment is proof that the
method can truly run in online mode and it also shows that considerable HAR model
performance gains can be registered even under realistic conditions.
The ndings from this research point to the conclusion that online learning from userprovided
annotations is a valid solution to the problem of constructing personalised
HAR models