Evidence map›Paper›PMID 32438649›Full record

ReviewInternational journal of environmental research and public health2020

Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics.

Harish Chander, Reuben F Burch, Purva Talegaonkar, David Saucier, Tony Luczak, John E Ball, Alana Turner, Sachini N K Kodithuwakku Arachchige, Will Carroll, Brian K Smith and 2 more

Abstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  19. Feasibility of Using Floor Vibration to Detect Human Falls.International journal of environmental research and public health · 2020
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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

12 authors.

Harish ChanderNeuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA.
Reuben F BurchDepartment of Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA.
Purva TalegaonkarDepartment of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
David SaucierDepartment of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
Tony LuczakNational Strategic Planning and Analysis Research Center (NSPARC), Mississippi State University, Mississippi State, MS 39762, USA.
John E BallDepartment of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
Alana TurnerNeuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA.
Sachini N K Kodithuwakku ArachchigeNeuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA.
Will CarrollDepartment of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
Brian K SmithDepartment of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
Adam KnightNeuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA.
Raj K PrabhuDepartment of Agricultural and Biomedical Engineering, Mississippi State University, Mississippi State, MS 39762, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of "Closing the Wearable Gap" journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

Indexed as

Accidental FallsWearable Electronic DevicesWorkplaceErgonomicsHumansMovementfall preventionhuman factorsmotion analysisoccupational falls.wearable devices

Identifiers

PMID32438649
PMCPMC7277680

What Socratic 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 Socratic graph.