Evidence map›Paper›PMID 37688021›Full record

ArticleSensors (Basel, Switzerland)2023

An IoT Sensing Platform and Serious Game for Remote Martial Arts Training.

Karlos Ishac, Katia Bourahmoune, Marc Carmichael

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Karlos IshacFaculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, Sydney, NSW 2007, Australia.ORCID 0000-0001-6215-3297
Katia BourahmouneFaculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, Sydney, NSW 2007, Australia.ORCID 0000-0001-5572-7811
Marc CarmichaelFaculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, Sydney, NSW 2007, Australia.ORCID 0000-0001-8439-0074

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a system for self-supported martial arts training using an IoT sensing platform and Serious Game that can also be extended for general sports training. In martial arts, it is important that the practitioner is correctly performing each technique to accurately learn and prevent injury. A common stance in all martial arts, but especially in Shaolin Kung Fu, is the horse stance or Mabu. With the pandemic, many more people adopted remote training without the presence of a professional trainer to give advice. Our developed LifeMat system, which is a novel IoT pressure-sensitive training mat, uses pressure maps and pattern recognition to accurately classify key martial arts postures, provide feedback on form, and detect when the user performs the technique incorrectly. This is presented in the form of a Serious Game we have developed named Kung Future that focuses on the Mabu stance as a case study. We tested 14 participants with three different feedback conditions and demonstrated that, on average, participants had higher performance, duration, engagement, and motivation when game feedback was active. Furthermore, user responses from the surveys suggested positive feedback for real-world and long-term use and scalability.

Indexed as

Martial ArtsAnimalsHorsesLearningMotivationPandemicsPosturebiomechanicsgamificationIoTKung Fumartial artssensingSerious Games

Identifiers

PMID37688021
PMCPMC10490566

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.