Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization.

Anjia Ye, Ananda Maiti, Matthew Schmidt, Scott J Pedersen

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Anjia YeSchool of Education, University of Tasmania, Launceston, TAS 7248, Australia.ORCID 0000-0001-5888-4994

Ananda MaitiSchool of IT, Deakin University, Waurn Ponds, VIC 3216, Australia.ORCID 0000-0003-0785-5282

Matthew SchmidtSchool of Health Sciences, University of Tasmania, Sandy Bay, TAS 7005, Australia.ORCID 0000-0001-9844-3296

Scott J PedersenSchool of Education, University of Tasmania, Launceston, TAS 7248, Australia.ORCID 0000-0002-8566-7693

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Occupational sedentary behavior presents a public health risk, yet current interventions often rely on subjective self-reports or context-blind prompts. This study validates a privacy-preserving, edge-computing time-of-flight (ToF) sensor that detects postural states and quantifies therapeutic exercise gestures in real time. The dual-sensor architecture distinguishes between sitting, standing, and absence, while capturing rapid sit-to-stand repetitions suitable for active-break interventions. In this paper, a laboratory study (

Indexed as

Sedentary BehaviorWearable Electronic DevicesAccelerometryExerciseHumansPostureedge computinghuman activity recognitionInternet of Things (IoT)sedentary behaviorworkplace health

Identifiers

PMID42076577

DOI10.3390/s26082468

PMCPMC13120596

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.