Evidence map›Paper›PMID 30002803›Full record

ArticleJournal of healthcare engineering2018

Study on Tripping Risks in Fast Walking through Cadence-Controlled Gait Analysis.

Wen-Fong Wang, Wei-Chih Lien, Che-Yu Liu, Ching-Yu Yang

Abstract read
In one paragraph

Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. The Effects of Real-Time Haptic Feedback on Gait and Cognitive Load in Older Adults.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025
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  3. Article
  4. Article
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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

4 authors.

Wen-Fong WangDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Yunlin 640, Taiwan.
Wei-Chih LienDepartment of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 600, Taiwan.ORCID 0000-0002-7679-8042
Che-Yu LiuDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Yunlin 640, Taiwan.
Ching-Yu YangDepartment of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong, Penghu 880, Taiwan.ORCID 0000-0001-9593-8728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fast walking is a common exercise for most people to promote health. However, a higher cadence due to fast walking on ordinary or uneven ground raises the risk of tripping. To investigate the tripping issue, research to observe the gait in fast walking is needed. To explore the relationship between fast gait and the risk of tripping, a gait recording system with a specific synchronization mechanism was developed in this work. The system can acquire gait signals from wearable sensors and action cameras at different cadences. Meanwhile, algorithms for gait cycle segmentation and characteristic extraction were proposed for analyzing a fast gait. In the gait analysis, the correlations of low, moderate, and high cadence in cueing and no cueing gaits were computed, and two results were obtained. First, the higher the cadence is, the larger the motion strength in the terminal foot swing will be and the smaller the motion strength at the starting foot swing. Second, the decreased distance of foot clearance becomes more conspicuous as the cadence increased, especially if one is walking more than 120 beats. The results indicate that fast walking with bigger strides and lower cadence is the best way to maintain safety in moving over ordinary ground.

Indexed as

Accidental FallsSignal Processing, Computer-AssistedAlgorithmsFootGait AnalysisHumansVideo RecordingWalking

Identifiers

PMID30002803
PMCPMC5996466

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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.