ChatGPT + Arduino - a powerful approach for unique use-case tailored sensors

Microcontroller development platforms, especially Arduinos have showed their viability in creating unique tailored low-cost Internet of Things (IoT) solutions. However, to take advantage of these platforms, moderate programming skills are needed. To address this barrier, a software tool called AutoCode AI for Arduino that utilizes the ChatGPT API to simplify Arduino programming was developed. This tool is designed to assist individuals, especially the novice, in resource-constrained environments by providing an intuitive platform for generating Arduino code. It integrates ChatGPT's AI capabilities with a graphical user interface, allowing users to input code snippets, request code combinations, and receive explanations and compilations through a constructive/systematic approach. Initial usability tests, conducted with participants of varying skill levels, demonstrated the tool's ability to generate functional code for tasks ranging from simp le to moderately complex. These findings highlight its utility as both an educational and a developmental resource in the IoT domain. By offering an accessible and effective solution, this tool contributes to the broader efforts in democratizing IoT development. Emphasizing practical applications, the combination of ChatGPT and Arduino offers a powerful approach for developing sensors uniquely tailored to specific use cases.

Authors

Institutions

Publication Details

Journal
IET conference proceedings.
Published
2024-10-01
DOI
https://doi.org/10.1049/icp.2024.3496
Citations
2
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ChatGPT + Arduino - a powerful approach for unique use-case tailored sensors

Antti Martikkala, Iñigo Flores Ituarte, Joe David
2 citations
IET conference proceedings.
COVID-19 diagnosis using AI
0.81
article

ChatGPT + Arduino - a powerful approach for unique use-case tailored sensors

Antti Martikkala, Iñigo Flores Ituarte, Joe David
article en
2 citations

Abstract

Microcontroller development platforms, especially Arduinos have showed their viability in creating unique tailored low-cost Internet of Things (IoT) solutions. However, to take advantage of these platforms, moderate programming skills are needed. To address this barrier, a software tool called AutoCode AI for Arduino that utilizes the ChatGPT API to simplify Arduino programming was developed. This tool is designed to assist individuals, especially the novice, in resource-constrained environments by providing an intuitive platform for generating Arduino code. It integrates ChatGPT's AI capabilities with a graphical user interface, allowing users to input code snippets, request code combinations, and receive explanations and compilations through a constructive/systematic approach. Initial usability tests, conducted with participants of varying skill levels, demonstrated the tool's ability to generate functional code for tasks ranging from simp le to moderately complex. These findings highlight its utility as both an educational and a developmental resource in the IoT domain. By offering an accessible and effective solution, this tool contributes to the broader efforts in democratizing IoT development. Emphasizing practical applications, the combination of ChatGPT and Arduino offers a powerful approach for developing sensors uniquely tailored to specific use cases.

IET conference proceedings.Vol. 2024(11)
Tampere University (FI)
Openalex Percentile: Top 24%
COVID-19 diagnosis using AI
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.