Evidence map›Paper›PMID 39771619›Full record

ReviewSensors (Basel, Switzerland)2024

Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.

Anna M Joseph, Azadeh Kian, Rezaul Begg

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
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

3 authors.

Anna M JosephInstitute for Health and Sport, Victoria University, Melbourne, VIC 3000, Australia.ORCID 0000-0002-6529-974X
Azadeh KianInstitute for Health and Sport, Victoria University, Melbourne, VIC 3000, Australia.ORCID 0000-0003-1724-790X
Rezaul BeggInstitute for Health and Sport, Victoria University, Melbourne, VIC 3000, Australia.ORCID 0000-0002-3195-8591

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) into smart shoes has proven effective for capturing detailed foot movements and spatiotemporal gait characteristics. While IMUs enable accurate foot trajectory estimation through the double integration of acceleration data, challenges such as drift errors necessitate robust correction techniques to ensure reliable performance. This review analyzes current literature on shoe-based systems utilizing IMUs to estimate spatiotemporal gait parameters and foot trajectory characteristics, including foot-ground clearance. We explore the challenges and advancements in achieving accurate 3D foot trajectory estimation using IMUs in smart shoes and the application of advanced techniques like zero-velocity updates and error correction methods. These developments present significant opportunities for achieving reliable and efficient real-time gait assessment in everyday environments.

Indexed as

GaitGait AnalysisShoesWearable Electronic DevicesAlgorithmsFootHumansSpatio-Temporal AnalysisWalkingfall preventionfoot clearancefoot movement trackingfoot trajectory estimationgait analysisinertial measurement unitportablesmart shoesspatiotemporal gait parameterswearable sensors

Identifiers

PMID39771619
PMCPMC11678955

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.