Evidence map›Paper›PMID 38002405›Full record

ArticleBioengineering (Basel, Switzerland)2023

Creating an AI-Enhanced Morse Code Translation System Based on Images for People with Severe Disabilities.

Chung-Min Wu, Yeou-Jiunn Chen, Shih-Chung Chen, Sheng-Feng Zheng

Open access · goldAbstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. 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
0.9field-weighted citation impact, top 27% of its field
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, 5 citations in OpenAlex.

  1. Article
  2. Review
  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

4 authors at 3 institutions in 1 country.

Chung-Min WuDepartment of Intelligent Automation Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan.ORCID 0000-0001-9075-2350
Yeou-Jiunn ChenDepartment of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710301, Taiwan.ORCID 0000-0002-9867-4788
Shih-Chung ChenDepartment of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710301, Taiwan.ORCID 0000-0001-6864-1080
Sheng-Feng ZhengDepartment of Intelligent Robotics Engineering, Kun Shan University, Tainan 710303, Taiwan.
Southern Taiwan University of Science and Technology · TWKun Shan University · TWNational Chin-Yi University of Technology · TW

Funding

the National Science and Technology Council (NSTC), Taiwan NSTC 111-2221-E-167-039 and MOST 111-2221-E-218-023
6 · The paper itself

Abstract

(1) Background: Patients with severe physical impairments (spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis) often have limited mobility due to physical limitations, and may even be bedridden all day long, losing the ability to take care of themselves. In more severe cases, the ability to speak may even be lost, making even basic communication very difficult. (2) Methods: This research will design a set of image-assistive communication equipment based on artificial intelligence to solve communication problems of daily needs. Using artificial intelligence for facial positioning, and facial-motion-recognition-generated Morse code, and then translating it into readable characters or commands, it allows users to control computer software by themselves and communicate through wireless networks or a Bluetooth protocol to control environment peripherals. (3) Results: In this study, 23 human-typed data sets were subjected to recognition using fuzzy algorithms. The average recognition rates for expert-generated data and data input by individuals with disabilities were 99.83% and 98.6%, respectively. (4) Conclusions: Through this system, users can express their thoughts and needs through their facial movements, thereby improving their quality of life and having an independent living space. Moreover, the system can be used without touching external switches, greatly improving convenience and safety.

Indexed as

artificial intelligencecaregivercommunicationdisabilitiesMorse code

Identifiers

PMID38002405
PMCPMC10669406
OpenAlexW4388304903

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.