Evidence map›Paper›PMID 32709269›Full record

ArticleMedical engineering & physics2020

Usability evaluation of attitude control for a robotic wheelchair for tip mitigation in outdoor environments.

Sivashankar Sivakanthan, Jorge L Candiotti, S Andrea Sundaram, Courtney Battles, Brandon J Daveler, Cheng-Shiu Chung, Garrett G Grindle, Rosemarie Cooper, Brad E Dicianno, Rory A Cooper

Open access · greenAbstract read
In one paragraph

Article in Medical engineering & physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
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

4 citing papers in PubMed, 13 citations in OpenAlex.

  1. User Field Testing for Robot Wheelchairs: A Mixed-Method Approach.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2025
    Article
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  3. Review
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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

10 authors at 2 institutions in 1 country.

Sivashankar SivakanthanHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Jorge L CandiottiHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
S Andrea SundaramHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Courtney BattlesHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Brandon J DavelerHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Cheng-Shiu ChungHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Garrett G GrindleHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Rosemarie CooperHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States.
Brad E DiciannoHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States; Department of Physical Medicine and Rehabilitation, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Rory A CooperHuman Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; University of Pittsburgh, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States. Electronic address: rcooper@pitt.edu.
University of Pittsburgh · USVA Pittsburgh Healthcare System · US

Funding

ACL HHS 90AR5021RRD VA I50 RX002998RRD VA IK6 RX003916
6 · The paper itself

Abstract

Tips and falls are the most prominent causes of wheelchair accidents that occur when driving on uneven terrains and less accessible environments. The Mobility Enhancement Robotic Wheelchair (MEBot) was designed to improve the stability of Electric Powered Wheelchairs (EPW) when driving over these environments. MEBot offers six independently height-adjustable wheels to control attitude of its seat over uneven and angled terrains. Its attitude control application uses an inertial measurement unit to detect seat angles changes to adjust each wheel-height accordingly. MEBot was compared to commercial EPWs in terms of EPW performance (seat angle changes and response time) and participant perception (satisfaction and task-load demand) towards each device. Ten participants drove their own EPW and MEBot for five trials each through driving tasks that replicated outdoor environments. Results showed less change in the pitch angle when driving up and down a 10° slope using MEBot (5.6 ± 1.6°, 6.6 ± 0.5°) compared to the participants' own EPW (14.6 ± 2.6°, 12.1 ± 2.6°). However, MEBot required 7.8 ± 3.0 s to self-adjust to the minimum angle when driving over the tasks. Participants reported no difference in satisfaction and task load demand between EPWs due to similarities in comfort and ease-of-use. Improving the speed and efficiency of MEBot's attitude control application will be addressed in future work based upon participants' feedback.

Indexed as

RoboticsRobotic Surgical ProceduresWheelchairsElectricityEquipment DesignHumansAccessibilityAssistive technologyAttitude control suspensionRoboticsTerrain negotiationWheelchairs

Identifiers

PMID32709269
PMCPMC10060049
OpenAlexW3041972762

What OpenQuestion holds

Textmetadata
LicenceTDM
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.