Evidence map›Paper›PMID 36366154›Full record

ArticleSensors (Basel, Switzerland)2022

Incremental Learning of Human Activities in Smart Homes.

Sook-Ling Chua, Lee Kien Foo, Hans W Guesgen, Stephen Marsland

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sook-Ling ChuaFaculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.ORCID 0000-0003-3081-2950
Lee Kien FooFaculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.
Hans W GuesgenSchool of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand.
Stephen MarslandSchool of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand.ORCID 0000-0002-9568-848X

Funding

Ministry of Education, Malaysia under the Fundamental Research Grant Scheme FRGS/1/2021/ICT02/MMU/02/2
6 · The paper itself

Abstract

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Indexed as

Human ActivitiesMachine LearningHumansactivity recognitionincremental learningnovelty detectionprediction by partial matchingsmart homes

Identifiers

PMID36366154
PMCPMC9656698

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.