Evidence map›Paper›PMID 35780600›Full record

ArticleComputers in biology and medicine2022

Moving towards intelligent telemedicine: Computer vision measurement of human movement.

Renjie Li, Rebecca J St George, Xinyi Wang, Katherine Lawler, Edward Hill, Saurabh Garg, Stefan Williams, Samuel Relton, David Hogg, Quan Bai and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
5.6field-weighted citation impact, top 4% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

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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

11 authors at 2 institutions in 2 countries.

Renjie LiDiscipline of Information and Communication Technology, Wicking Dementia Research and Education Centre, University of Tasmania, Australia. Electronic address: renjie.li@utas.edu.au.
Rebecca J St GeorgeSensorimotor Neuroscience and Aging Group, School of Psychological Sciences, University of Tasmania, Australia. Electronic address: rebecca.stgeorge@utas.edu.au.
Xinyi WangDiscipline of Information and Communication Technology, Wicking Dementia Research and Education Centre, University of Tasmania, Australia. Electronic address: xinyi.wang@utas.edu.au.
Katherine LawlerWicking Dementia Research and Education Centre, University of Tasmania, Australia. Electronic address: katherine.lawler@utas.edu.au.
Edward HillWicking Dementia Research and Education Centre, University of Tasmania, Australia. Electronic address: edward.hill@utas.edu.au.
Saurabh GargDiscipline of Information and Communication Technology, University of Tasmania, Australia. Electronic address: saurabh.garg@utas.edu.au.
Stefan WilliamsSchool of Medicine, University of Leeds, United Kingdom. Electronic address: umswi@leeds.ac.uk.
Samuel ReltonSchool of Medicine, University of Leeds, United Kingdom. Electronic address: S.D.Relton@leeds.ac.uk.
David HoggSchool of Computing, University of Leeds, United Kingdom. Electronic address: D.C.Hogg@leeds.ac.uk.
Quan BaiDiscipline of Information and Communication Technology, University of Tasmania, Australia. Electronic address: quan.bai@utas.edu.au.
Jane AltyWicking Dementia Research and Education Centre, University of Tasmania, Australia. Electronic address: jane.alty@utas.edu.au.
University of Tasmania · AUUniversity of Leeds · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTelemedicine video consultations are rapidly increasing globally, accelerated by the COVID-19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual judgement because video cameras are so ubiquitous in personal devices and new techniques, such as DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations.

objectivesTo determine the validity and reliability of DLC applied to laptop videos to measure finger tapping, a validated test of human movement.

methodSixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak, a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101, ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking.

resultsOver 96% (529/552) of DLC measures were within +/-0.5 Hz of the Optotrak measures. At tapping frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with the laptop camera's low FPS. Computer vision methods hold potential for moving us towards intelligent telemedicine by providing human movement analysis during consultations. However, further developments are required to accurately measure the fastest movements.

Indexed as

COVID-19TelemedicineAdultComputersHumansMovementPandemicsReproducibility of ResultsComputer visionDeepLabCutFinger tappingMotor controlTelemedicine

Identifiers

PMID35780600
PMCPMC9428734
OpenAlexW4283219131

What OpenQuestion holds

Textmetadata
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.