Evidence map›Paper›PMID 33171710›Full record

ArticleSensors (Basel, Switzerland)2020

A Pedestrian Dead Reckoning Method for Head-Mounted Sensors.

Xinyu Hou, Jeroen Bergmann

Abstract readLetter
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors.Sensors (Basel, Switzerland) · 2022
    Article
  4. Article
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

2 authors.

Xinyu HouNatural Interactions Lab, Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK.ORCID 0000-0002-8292-851X
Jeroen BergmannNatural Interactions Lab, Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK.ORCID 0000-0001-7306-2630

Funding

Engineering and Physical Sciences Research Council EPSRC Impact Acceleration Account Award EP/R511742/1
6 · The paper itself

Abstract

Pedestrian dead reckoning (PDR) plays an important role in modern life, including localisation and navigation if a Global Positioning System (GPS) is not available. Most previous PDR methods adopted foot-mounted sensors. However, humans have evolved to keep the head steady in space when the body is moving in order to stabilise the visual field. This indicates that sensors that are placed on the head might provide a more suitable alternative for real-world tracking. Emerging wearable technologies that are connected to the head also makes this a growing field of interest. Head-mounted equipment, such as glasses, are already ubiquitous in everyday life. Whilst other wearable gear, such as helmets, masks, or mouthguards, are becoming increasingly more common. Thus, an accurate PDR method that is specifically designed for head-mounted sensors is needed. It could have various applications in sports, emergency rescue, smart home, etc. In this paper, a new PDR method is introduced for head mounted sensors and compared to two established methods. The data were collected by sensors that were placed on glasses and embedded into a mouthguard. The results show that the newly proposed method outperforms the other two techniques in terms of accuracy, with the new method producing an average end-to-end error of 0.88 m and total distance error of 2.10%.

Indexed as

PedestriansWalkingWearable Electronic DevicesAlgorithmsHeadHumansinertial measurement unitnavigationsmart glassesvirtual realitywearable sensors

Identifiers

PMID33171710
PMCPMC7664376

What OpenQuestion holds

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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.