Evidence map›Paper›PMID 37250953›Full record

ArticleResearch (Washington, D.C.)2023

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports.

Zi Hao Guo, ZiXuan Zhang, Kang An, Tianyiyi He, Zhongda Sun, Xiong Pu, Chengkuo Lee

Abstract read
In one paragraph

Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  3. Curvy Surface Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  10. Review
  11. Article
  12. AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. 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

7 authors.

Zi Hao GuoBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, People's Republic of China.
ZiXuan ZhangDepartment of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.
Kang AnSchool of Mechanical and Materials Engineering, North China University of Technology, Beijing 100144, People's Republic of China.
Tianyiyi HeDepartment of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.
Zhongda SunDepartment of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.
Xiong PuBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, People's Republic of China.
Chengkuo LeeDepartment of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Regular exercise paves the way to a healthy life. However, conventional sports events are susceptible to weather conditions. Current motion sensors for home-based sports are mainly limited by operation power consumption, single-direction sensitivity, or inferior data analysis. Herein, by leveraging the 3-dimensional printing technique and triboelectric effect, a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory. By integrating with a belt, this sensor could be used to identify some low degree of freedom motions, e.g., waist or gait motion, with a high accuracy of 93.8%. Furthermore, when wearing the sensor at the ankle position, signals generated from shank motions that contain more abundant information could also be effectively collected. By means of a deep learning algorithm, the kicking direction and force could be precisely differentiated with an accuracy of 97.5%. Toward practical application, a virtual reality-enabled fitness game and a shooting game were successfully demonstrated. This work is believed to open up new insights for the development of future household sports or rehabilitation.

Identifiers

PMID37250953
PMCPMC10211429

What OpenQuestion holds

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Registered trials

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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.