Evidence map›Paper›PMID 38486898›Full record

ArticleWearable technologies2022

Gait monitoring for older adults during guided walking: An integrated assistive robot and wearable sensor approach.

Qingya Zhao, Zhuo Chen, Corey D Landis, Ashley Lytle, Ashwini K Rao, Damiano Zanotto, Yi Guo

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Wearable technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Qingya ZhaoDepartment of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.
Zhuo ChenDepartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.
Corey D LandisDepartment of Rehabilitation & Regenerative Medicine (Programs in Physical Therapy G.H. Sergievsky Center), Columbia University, New York, NY, USA.
Ashley LytleCollege of Arts and Letters, Stevens Institute of Technology, Hoboken, NJ, USA.
Ashwini K RaoDepartment of Rehabilitation & Regenerative Medicine (Programs in Physical Therapy G.H. Sergievsky Center), Columbia University, New York, NY, USA.
Damiano ZanottoDepartment of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.ORCID https://orcid.org/0000-0003-3514-6889
Yi GuoDepartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.ORCID https://orcid.org/0000-0002-7158-351X
Stevens Institute of Technology · USColumbia University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An active lifestyle can mitigate physical decline and cognitive impairment in older adults. Regular walking exercises for older individuals result in enhanced balance and reduced risk of falling. In this article, we present a study on gait monitoring for older adults during walking using an integrated system encompassing an assistive robot and wearable sensors. The system fuses data from the robot onboard Red Green Blue plus Depth (RGB-D) sensor with inertial and pressure sensors embedded in shoe insoles, and estimates spatiotemporal gait parameters and dynamic margin of stability in real-time. Data collected with 24 participants at a community center reveal associations between gait parameters, physical performance (evaluated with the Short Physical Performance Battery), and cognitive ability (measured with the Montreal Cognitive Assessment). The results validate the feasibility of using such a portable system in out-of-the-lab conditions and will be helpful for designing future technology-enhanced exercise interventions to improve balance, mobility, and strength and potentially reduce falls in older adults.

Indexed as

assistive robotcognitive assessmentdynamic margin of stabilitygait analysisinstrumented footwear

Identifiers

PMID38486898
PMCPMC10936390
OpenAlexW4312326984

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.