Gesture-Controlled Music Player Using Hand Gestures

This paper presents a gesture-controlled music player system that interprets real-time hand gestures to control media functions such as play, pause, next/previous track, and volume adjustment.The project aims to deliver a touchless user experience, offering intuitive control of digital media via simple hand movements detected through a webcam.The system utilises computer vision technologies, including Opencv, MediaPipe, and Python's gesture recognition algorithms.Designed with accessibility and hygiene in mind, this music player is particularly relevant in environments where hands-free interaction is preferred or necessary.Testing showed gesture accuracy of over 90% under good lighting conditions, confirming the system's practical usability.Future enhancements will focus on integrating machine learning for personalised gesture mapping and expanding crossplatform support.

Publication Details

Journal
IJIREEICE
Published
2025-05-20
DOI
https://doi.org/10.17148/ijireeice.2025.13515
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Gesture-Controlled Music Player Using Hand Gestures

IJIREEICE
Hand Gesture Recognition Systems
article

Gesture-Controlled Music Player Using Hand Gestures

article en

Abstract

This paper presents a gesture-controlled music player system that interprets real-time hand gestures to control media functions such as play, pause, next/previous track, and volume adjustment.The project aims to deliver a touchless user experience, offering intuitive control of digital media via simple hand movements detected through a webcam.The system utilises computer vision technologies, including Opencv, MediaPipe, and Python's gesture recognition algorithms.Designed with accessibility and hygiene in mind, this music player is particularly relevant in environments where hands-free interaction is preferred or necessary.Testing showed gesture accuracy of over 90% under good lighting conditions, confirming the system's practical usability.Future enhancements will focus on integrating machine learning for personalised gesture mapping and expanding crossplatform support.

IJIREEICEVol. 13(5)
Openalex Percentile: Top 89%
Hand Gesture Recognition Systems
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