Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition.

Parastoo Alinia, Asiful Arefeen, Zhila Esna Ashari, Seyed Iman Mirzadeh, Hassan Ghasemzadeh

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Cost-Effective Multitask Active Learning in Wearable Sensor Systems.Sensors (Basel, Switzerland) · 2025
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

5 authors.

Parastoo AliniaSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.ORCID 0000-0001-8201-3005

Asiful ArefeenCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.

Zhila Esna AshariSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.

Seyed Iman MirzadehSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.

Hassan GhasemzadehCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.

Funding

National Science Foundation CNS-1750679National Science Foundation CNS-2210133

6 · The paper itself

Abstract

Activity recognition using data collected with smart devices such as mobile and wearable sensors has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of smart devices in the internet-of-things era has limited the adoption of activity recognition models for use across different devices. This lack of cross-domain adaptation is particularly notable across sensors of different modalities where the mapping of the sensor data in the traditional feature level is highly challenging. To address this challenge, we propose

Indexed as

AlgorithmsMachine LearningHumansactivity recognitionmachine learningmobile healthmodel-independentstructural similaritytransfer learningwearables

Identifiers

PMID37514630

DOI10.3390/s23146337

PMCPMC10385762

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.