Evidence map›Paper›PMID 41695597›Full record

ArticleHealthcare technology letters

An Add-On Contactless Measurement System for Monitoring Driving Behaviours in Motorised Mobility Scooters.

Yi Liu, Takenobu Inoue, Jun Suzurikawa

Abstract read
In one paragraph

Article in Healthcare technology letters. 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.

  1. 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

3 authors.

Yi LiuCollege of Mechanical and Electrical Engineering Harbin Engineering University Harbin China.
Takenobu InoueDepartment of Assistive Technology, Research Institute National Rehabilitation Center For Persons with Disabilities Tokorozawa Japan.
Jun SuzurikawaDepartment of Assistive Technology, Research Institute National Rehabilitation Center For Persons with Disabilities Tokorozawa Japan.ORCID https://orcid.org/0000-0001-7987-6811

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing use of motorised mobility scooters (MMSs) has raised significant safety concerns, particularly related to user behaviour during operation. Although various advanced driving assistance systems have been incorporated into MMSs to identify potential environmental hazards, few studies have investigated the impact of user behaviour in MMS driving. This study was the first to incorporate automated behaviour monitoring into the evaluation of MMS driving using an add-on driving behaviour monitoring system (ADBMS). The ADBMS was a platform for contactless measurement that used a pre-trained convolutional neural network to estimate posture and two inertial measurement units to record steering and throttle operations. Experiments were conducted to demonstrate the usability of the ADBMS and evaluate the coordination of head movements and steering manoeuvres when driving an MMS in the outdoor environment. Cross-correlation analysis revealed that head movement consistently preceded steering operation during the driving tasks, indicating the potential of the proposed system to quantify user behaviour related to attention toward the surrounding environment. The lag time between these two parameters may serve as a novel index of driving safety. These findings could support a comprehensive understanding of users' driving behaviours and provide valuable insights into developing behavioural interventions to promote safer MMS use.

Indexed as

accelerometersimage motion analysismotion measurementpatient rehabilitationwheelchairs

Identifiers

PMID41695597
PMCPMC12894081

What OpenQuestion holds

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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.